ALBERT

Description: The worldwide Sensor Web comprises observation data from diverse sources. Each data provider may process and assess datasets differently before making them available online. This information is often invisible to end users. Therefore, publishing observation data with quality descriptions is vital as it helps users to assess the suitability of data for their applications. It is also important to capture contextual information concerning data quality such as provenance to trace back incorrect data to its origins. In the Open Geospatial Consortium (OGC)’s Sensor Web Enablement (SWE) framework, there is no sufficiently and practically applicable approach how these aspects can be systematically represented and made accessible. This paper presents Q-SOS—an extension of the OGC’s Sensor Observation Service (SOS) that supports retrieval of observation data together with quality descriptions. These descriptions are represented in an observation data model covering various aspects of data quality assessment. The service and the data model have been developed based on open standards and open source tools, and are productively being used to share observation data from the TERENO observatory infrastructure. We discuss the advantages of deploying the presented solutions from data provider and consumer viewpoints. Enhancements applied to the related open-source developments are also introduced.

Description: Recent disasters, such as the 2010 Haiti earthquake, have drawn attention to the potential role of citizens as active information producers. By using location-aware devices such as smartphones to collect geographic information in the form of geo-tagged text, photos, or videos, and sharing this information through online social media, such as Twitter, citizens create Volunteered Geographic Information (VGI). To effectively use this information for disaster management, we developed a VGI framework for the discovery of VGI. This framework consists of four components: (i) a VGI brokering module to provide a standard service interface to retrieve VGI from multiple resources based on spatial, temporal, and semantic parameters; (ii) a VGI quality control component, which employs semantic filtering and cross-referencing techniques to evaluate VGI; (iii) a VGI publisher module, which uses a service-based delivery mechanism to disseminate VGI, and (iv) a VGI discovery component to locate, browse, and query metadata about available VGI datasets. In a case study we employed a FOSS (Free and Open Source Software) strategy, open standards/specifications, and free/open data to show the utility of the framework. We demonstrate that the framework can facilitate data discovery for disaster management. The addition of quality metrics and a single aggregated source of relevant crisis VGI will allow users to make informed policy choices that could save lives, meet basic humanitarian needs earlier, and perhaps limit environmental and economic damage.

Description: As countries become increasingly urbanized, understanding how urban areas are changing within the landscape becomes increasingly important. Urbanized areas are often the strongest indicators of human interaction with the environment, and understanding how urban areas develop through remotely sensed data allows for more sustainable practices. A Landsat satellite sensor which is a remote sensing platform, with its ability to analyze global data, rapidly present itself as being an invaluable tool for studying the growth of urban areas. In this study, we present the virtual geo-library as the geovisualization tools to provide the analytical studies of the urbanization process in Malang City, East Java, Indonesia, using images derived from Landsat sensor family (1989 to 2014). We provide a dynamic geovisualization through virtual geo-library, where users could understand and get valuable scientific information (e.g., urban area changes and land use transformation in higher land). This system is also equipped with the tools to enable users to create automatic cartographic maps and print the results out as a digital pdf format file.

Description: Social media data have emerged as a new source for detecting and monitoring disaster events. A number of recent studies have suggested that social media data streams can be used to mine actionable data for emergency response and relief operation. However, no effort has been made to classify social media data into stages of disaster management (mitigation, preparedness, emergency response, and recovery), which has been used as a common reference for disaster researchers and emergency managers for decades to organize information and streamline priorities and activities during the course of a disaster. This paper makes an initial effort in coding social media messages into different themes within different disaster phases during a time-critical crisis by manually examining more than 10,000 tweets generated during a natural disaster and referencing the findings from the relevant literature and official government procedures involving different disaster stages. Moreover, a classifier based on logistic regression is trained and used for automatically mining and classifying the social media messages into various topic categories during various disaster phases. The classification results are necessary and useful for emergency managers to identify the transition between phases of disaster management, the timing of which is usually unknown and varies across disaster events, so that they can take action quickly and efficiently in the impacted communities. Information generated from the classification can also be used by the social science research communities to study various aspects of preparedness, response, impact and recovery.

Description: Building Information Models (e.g., IFC) and virtual 3D city models (e.g., CityGML) are revolutionising the way we manage information about our cities. However, the main focus of these models is on the physical and functional characteristics of urban properties and facilities, which neglects the legal and ownership aspects. In contrast, cadastral data models, such as the Land Administration Domain Model (LADM), have been developed for legal information management purposes and model legal objects such as ownership boundaries without providing correspondence to the object’s physical attributes. Integration of legal and physical objects in the virtual 3D city and cadastral models would maximise their utility and flexibility to support different applications that require an integrated resource of both legal and physical information, such as urban space management and land development processes. The aim of this paper is to propose a data model that supports both legal and physical information of urban environments. The methodology to develop this data model is to extend the core cadastral data model and integrate urban features into the data model. The outcome of the research can be utilised to extend the current data models to increases their usability for different applications that require both legal and physical information.

Description: Due to its relatively high availability and low cost, location-based social network (LBSN) (e.g., Foursquare) data (a popular type of volunteered geographic information) seem to be an alternative or complement to survey data in the study of travel behavior and activity analysis. Illustrating this situation, recently, a number of studies attempted to use LBSN data (e.g., Foursquare check-ins) to investigate patterns of human travel and activity. Of particular note is that compared to other individual-level characteristics of users, such as age, profession, education, income and so forth, gender is relatively highly available in the profiles of Foursquare users. Moreover, considering gender differences in travel and activity analysis is a popular research topic and is helpful in better understanding the changes in women’s roles in family, labor force participation, society and so forth. Therefore, this paper empirically investigates how gender influences the travel and activity patterns of active local Foursquare users in New York City. Empirical investigations of gender differences in travel and activity patterns are conducted at both the individual and aggregate level. The empirical results reveal that there are gender differences in the travel and activity patterns of active local users in New York City at both the individual and aggregate level. Finally, the results of the empirical study and the extent to which LBSN data can be exploited to produce travel diary data are discussed.

Description: The integration of user-generated content made in a collaborative environment is being increasingly considered a valuable input to reference maps, even from official map agencies such as USGS and Ordnance Survey. In Brazil, decades of lack of investment has resulted in a topographic map coverage that is both outdated and unequally distributed throughout the territory. This paper aims to analyze the spatial distribution of updates of OpenStreetMap in rural and urban areas in the country to understand the patterns of user updates and its correlation with other economic and developmental variables. This analysis will contribute to generating the knowledge needed in order to consider the use of this data as part of a reference layer of the National Spatial Database Infrastructure as well to design strategies to encourage user action in specific areas.

Description: When humanitarian workers embark on learning and dialogue for linking geoinformation to disaster management, the activities they confront are usually more difficult than interesting. How to accelerate the acquisition and deployment of skills and tools for spatial data collection and analysis, given the increasingly unmanageable workload of humanitarians? How to engage practitioners in experiencing the value and limitations of newly available tools? This paper offers an innovative approach to immerse disaster managers in geoinformation: participatory games that enable stakeholders to experience playable system dynamic models linking geoinformation, decisions and consequences in a way that is both serious and fun. A conceptual framework outlines the foundations of experiential learning through gameplay, with clear connections to a well-established risk management framework. Two case studies illustrate this approach: one involving flood management in the Zambezi river in southern Africa through the game UpRiver (in both physical and digital versions), and another pertaining to World Bank training on open data for resilience that combines applied improvisation activities with the need to understand and deploy software tools like Open Street Map and InaSAFE to manage school investments and schoolchildren evacuation in a simulated flood scenario for the city of La Plata, Argentina.

Description: This study modeled the urban growth in the Greater Cairo Region (GCR), one of the fastest growing mega cities in the world, using remote sensing data and ancillary data. Three land use land cover (LULC) maps (1984, 2003 and 2014) were produced from satellite images by using Support Vector Machines (SVM). Then, land cover changes were detected by applying a high level mapping technique that combines binary maps (change/no-change) and post classification comparison technique. The spatial and temporal urban growth patterns were analyzed using selected statistical metrics developed in the FRAGSTATS software. Major transitions to urban were modeled to predict the future scenarios for year 2025 using Land Change Modeler (LCM) embedded in the IDRISI software. The model results, after validation, indicated that 14% of the vegetation and 4% of the desert in 2014 will be urbanized in 2025. The urban areas within a 5-km buffer around: the Great Pyramids, Islamic Cairo and Al-Baron Palace were calculated, highlighting an intense urbanization especially around the Pyramids; 28% in 2014 up to 40% in 2025. Knowing the current and estimated urbanization situation in GCR will help decision makers to adjust and develop new plans to achieve a sustainable development of urban areas and to protect the historical locations.

Description: This paper presents an integrated framework for exploratory multi-scale spatio-temporal feature extraction and clustering of spatio-temporal data. The framework combines the multi-scale spatio-temporal decomposition, feature identification, feature enhancing and clustering in a unified process. The original data are firstly reorganized as multi-signal time series, and then decomposed by the multi-signal wavelet. Exploratory data analysis methods, such as histograms, are used for feature identification and enhancing. The spatio-temporal evolution process of the multi-scale features can then be tracked by the feature clusters based on the data adaptive Fuzzy C-Means Cluster. The approach was tested with the global 0.25° satellite altimeter data over a period of 21 years from 1993 to 2013. The tracking of the multi-scale spatio-temporal evolution characteristics of the 1997–98 strong El Niño were used as validation. The results show that our method can clearly reveal and track the spatio-temporal distribution and evolution of complex geographical phenomena. Our approach is efficient for global scale data analysis, and can be used to explore the multi-scale pattern of spatio-temporal processes.

Description: Information flows on social media platforms are able to show trends and user interests as well as connections between users. In this paper, we present a method how to analyze city related networks on the social media platform Twitter based on the user content. Forty million tweets have been downloaded via Twitter’s REST API (application programming interface) and Twitter’s Streaming API. The investigation focuses on two aspects: firstly, trend detection has been done to analyze 31 informational world cities, according the user activity, popularity of shared websites and topics defined by hashtags. Secondly, a hint of how connected informational cities are to each other is given by creating a clustered network based on the number of connections between different city pairs. Tokyo, New York City, London and Paris clearly lead the ranking of the most active cities if compared by the total number of tweets. The investigation shows that Twitter is very frequently used to share content from other services like Instagram or YouTube. The most popular topics in tweets reveal great differences between the cities. In conclusion, the investigation shows that social media services like Twitter also can be a mirror of the society they are used in and bring to light information flows of connected cities in a global network. The presented method can be applied in further research to analyze information flows regarding specific topics and/or geographical locations.

Description: In this paper, a scheme is presented for fusing a foot-mounted Inertial Measurement Unit (IMU) and a floor map to provide ubiquitous positioning in a number of settings, such as in a supermarket as a shopping guide, in a fire emergency service for navigation, or with a hospital patient to be tracked. First, several Zero-Velocity Detection (ZDET) algorithms are compared and discussed when used in the static detection of a pedestrian. By introducing information on the Zero Velocity of the pedestrian, fused with a magnetometer measurement, an improved Pedestrian Dead Reckoning (PDR) model is developed to constrain the accumulating errors associated with the PDR positioning. Second, a Correlation Matching Algorithm based on map projection (CMAP) is presented, and a zone division of a floor map is demonstrated for fusion of the PDR algorithm. Finally, in order to use the dynamic characteristics of a pedestrian’s trajectory, the Adaptive Unscented Kalman Filter (A-UKF) is applied to tightly integrate the IMU, magnetometers and floor map for ubiquitous positioning. The results of a field experiment performed on the fourth floor of the School of Environmental Science and Spatial Informatics (SESSI) building on the China University of Mining and Technology (CUMT) campus confirm that the proposed scheme can reliably achieve meter-level positioning.

Description: The world is urbanizing at a very fast pace. Modern geography, particularly geo-information systems (GIS) and global positioning systems (GPS) are reshaping the way urban and transport planners are collecting, exploring, synthesizing, analyzing, evaluating and presenting their data. [...]

Description: While free and open source software becomes increasingly important in geospatial research and industry, open science perspectives are generally less reflected in universities’ educational programs. We present an example of how free and open source software can be incorporated into geospatial education to promote open and reproducible science. Since 2008 graduate students at North Carolina State University have the opportunity to take a course on geospatial modeling and analysis that is taught with both proprietary and free and open source software. In this course, students perform geospatial tasks simultaneously in the proprietary package ArcGIS and the free and open source package GRASS GIS. By ensuring that students learn to distinguish between geospatial concepts and software specifics, students become more flexible and stronger spatial thinkers when choosing solutions for their independent work in the future. We also discuss ways to continually update and improve our publicly available teaching materials for reuse by teachers, self-learners and other members of the GIS community. Only when free and open source software is fully integrated into geospatial education, we will be able to encourage a culture of openness and, thus, enable greater reproducibility in research and development applications.

Description: The aims of this study were: (i) the mapping of asbestos cement roofs in an urban area; and (ii) the development of a spectral index related to the roof weathering status. Aerial images were collected through the Multispectral Infrared and Visible Imaging Spectrometer (MIVIS) sensor, which acquires data in 102 channels from the visible to the thermal infrared spectral range. An image based supervised classification was performed using the Spectral Angle Mapper (SAM) algorithm. The SAM was trained through a set of pixels selected on roofs of different materials. The map showed an average producer’s accuracy (PA) of 86% and a user’s accuracy (UA) of 89% for the asbestos cement class. A novel spectral index, the “Index of Surface Deterioration” (ISD), was defined based on measurements collected with a portable spectroradiometer on asbestos cement roofs that were characterized by different weathering statuses. The ISD was then calculated on the MIVIS images, allowing the distinction of two weathering classes (i.e., high and low). The asbestos cement map was handled in a Geographic Information System (GIS) in order to supply the municipalities with the cadastral references of each property having an asbestos cement roof. This tool can be purposed for municipalities as an aid to prioritize asbestos removal, based on roof weathering status.

Description: Shallow coral reefs threatened by climate change must be spatio-temporally analyzed in terms of their protection of coastal human populations. This study combines Japanese spatio-temporal gradients of population/asset and coral buffering exposure to stress-inducing and stress-mitigating factors so that the socio-economic and ecological (SEE) resilience tied to coral reefscapes can be regionally mapped (1200 km) at a fine resolution (1 arcsec) over a decade (11 years). Fuzzy logic was employed to associated environmental factors based on the related population/asset/coral buffering responses, as found in the literature. Once the factors were weighted according to their resilience contributions, temporally static patterns were evident: (1) a negative correlation occurs between coral buffering resilience and latitude; (2) the least resilient islands are low-lying, deprived of wide reef barriers, and located on the eastern and southern boundaries of the Nansei archipelago; (3) the southwestern-most, middle and northeastern-most islands have the same SEE resilience; and (4) Sekisei Lagoon islands have a very high coral buffering resilience. To overcome uncertainty, future studies should focus on the socio-ecological adaptive capacity, fine-scale ecological processes (such as coral and fish functional groups) and the prediction of the flood risks in the coming decades.

Description: Creating as-built plans of building interiors is a challenging task. In this paper we present a semi-automatic modelling system for creating residential building interior plans and their integration with existing map data to produce building models. Taking a set of imprecise measurements made with an interactive mobile phone room mapping application, the system performs spatial adjustments in accordance with soft and hard constraints imposed on the building plan geometry. The approach uses an optimisation model that exploits a high accuracy building outline, such as can be found in topographic map data, and the building topology to improve the quality of interior measurements and generate a standardised output. We test our system on building plans of five residential homes. Our evaluation shows that the approach enables construction of accurate interior plans from imprecise measurements. The experiments report an average accuracy of 0.24 m, close to the 0.20 m recommended by the CityGML LoD4 specification.

Description: In this paper, we propose a navigation approach for smartphones that enables visitors of major events to avoid crowded areas or narrow streets and to navigate out of dense crowds quickly. Two types of sensor data are integrated. Real-time optical images acquired and transmitted by an airborne camera system are used to compute an estimation of a crowd density map. For this purpose, a patch-based approach with a Gabor filter bank for texture classification in combination with an interest point detector and a smoothing function is applied. Furthermore, the crowd density is estimated based on location and movement speed of in situ smartphone measurements. This information allows for the enhancement of the overall crowd density layer. The composed density information is input to a least-cost routing workflow. Two possible use cases are presented, namely (i) an emergency application and (ii) a basic routing application. A prototypical implementation of the system is conducted as proof of concept. Our approach is capable of increasing the security level for major events. Visitors are able to avoid dense crowds by routing around them, while security and rescue forces are able to find the fastest way into the crowd.

Description: Various themed streets have recently been developed by local governments in order to stimulate local economies and to establish the identity of the corresponding places. However, the motivations behind the development of some of these themed street projects has been based on profit, without full considerations of people’s perceptions of their local areas, resulting in marginal effects on the local economies concerned. In response to this issue, this study proposed a themed street clustering method to detect the themed streets of a specific region, focusing on the commercial themed street, which is more prevalent than other types of themed streets using location based service data. This study especially uses “the street segment” as a basic unit for analysis. The Sillim and Gangnam areas of Seoul, South Korea were chosen for the evaluation of the adequacy of the proposed method. By comparing trade areas that were sourced from a market analysis report by a reliable agent with the themed streets detected in this study, the experiment results showed high proficiency of the proposed method.

Description: Delimitating trade areas is a major business concern. Today, mobile communication technologies make it possible to use social media data for this purpose. Few studies however, have focused on methods to extract suitable samples from social media data for trade area delimitation. In our case study, we divided Beijing into regular grid cells and extracted activity centers for each social media user. Ten sample sets were obtained by selecting users based on the retail agglomerations they visited and aggregating user activity centers to each grid cell. We calculated distance and visitation frequency attributes for each user and each grid cell. The distance value of a grid cell is the average distance of user activity centers in this grid cell to a retail agglomeration. The visitation frequency of a grid cell refers to the average count of visits to retail agglomerations by user activity centers for a cell. The calculated attribute values of 10 sets were input into a Huff model and the delimitated trade areas were evaluated. Results show that sets obtained by aggregating user activity centers have a better delimitating effect than sets obtained without aggregation. Differences in the distribution and intensity of trade areas also became apparent.

Description: Road information is fundamental not only in the military field but also common daily living. Automatic road extraction from a remote sensing images can provide references for city planning as well as transportation database and map updating. However, owing to the spectral similarity between roads and impervious structures, the current methods solely using spectral characteristics are often ineffective. By contrast, the detailed information discernible from the high-resolution aerial images enables road extraction with spatial texture features. In this study, a knowledge-based method is established and proposed; this method incorporates the spatial texture feature into urban road extraction. The spatial texture feature is initially extracted by the local Moran’s I, and the derived texture is added to the spectral bands of image for image segmentation. Subsequently, features like brightness, standard deviation, rectangularity, aspect ratio, and area are selected to form the hypothesis and verification model based on road knowledge. Finally, roads are extracted by applying the hypothesis and verification model and are post-processed based on the mathematical morphology. The newly proposed method is evaluated by conducting two experiments. Results show that the completeness, correctness, and quality of the results could reach approximately 94%, 90% and 86% respectively, indicating that the proposed method is effective for urban road extraction.

Description: Effectively identifying an airport from satellite and aerial imagery is a challenging task. Traditional methods mainly focus on the use of multiple features for the detection of runways and some also adapt knowledge of airports, but the results are unsatisfactory and the usage limited. A new method is proposed to recognize airports from high-resolution optical images. This method involves the analysis of the saliency distribution and the use of fuzzy rule-based classification. First, a number of images with and without airports are segmented into multiple scales to obtain a saliency distribution map that best highlights the saliency distinction between airports and other objects. Then, on the basis of the segmentation result and the structural information of airports, we analyze the segmentation result to extract and represent the semantic information of each image via the bag-of-visual-words (BOVW) model. The image correlation degree is combined with the BOVW model and fractal dimension calculation to make a more complete description of the airports and to carry out preliminary classification. Finally, the support vector machine (SVM) is adopted for detailed classification to classify the remaining imagery. The experiment shows that the proposed method achieves a precision of 89.47% and a recall of 90.67% and performs better than other state of the art methods on precision and recall.

Description: An open, standardized data management and related service infrastructure is a crucial requirement for a seamless storage and exchange of data and information within research projects, for the dissemination of project results and for their application in decision making processes. However, typical project databases often refer to only one research project and are limited to specific purposes. Once implemented, those systems are often not further maintained and updated, rendering the stored information useless once the system stops operating. The River Basin Information System (RBIS) presented here is designed to fit not only the requirements of one research project, but focuses on generic functions, extensibility and standards compliance typically found in interdisciplinary environmental research. Developed throughout more than 10 years of research cooperation worldwide, RBIS is designed to manage different types of environmental data with and without spatial context together with a rich set of metadata. Beside data management and storage, RBIS provides functions for the visualization, linking, analysis and processing of different types of data to support research, decision making, result dissemination and information discovery for all kinds of users. The focus of this paper is on the description of the technical implementation and the presentation of functions. This will be complemented by an overview of example applications and experiences during RBIS development and operation.

Description: This study uses a large-scale mobile phone dataset to estimate potential demand of bicycle trips in a city. By identifying two important anchor points (night-time anchor point and day-time anchor point) from individual cellphone trajectories, this study proposes an anchor-point based trajectory segmentation method to partition cellphone trajectories into trip chain segments. By selecting trip chain segments that can potentially be served by bicycles, two indicators (inflow and outflow) are generated at the cellphone tower level to estimate the potential demand of incoming and outgoing bicycle trips at different places in the city and different times of a day. A maximum coverage location-allocation model is used to suggest locations of bike sharing stations based on the total demand generated at each cellphone tower. Two measures are introduced to further understand characteristics of the suggested bike station locations: (1) accessibility; and (2) dynamic relationships between incoming and outgoing trips. The accessibility measure quantifies how well the stations could serve bicycle users to reach other potential activity destinations. The dynamic relationships reflect the asymmetry of human travel patterns at different times of a day. The study indicates the value of mobile phone data to intelligent spatial decision support in public transportation planning.

Description: With the advance of the World-Wide Web (WWW) technology, people can easily share content on the Web, including geospatial data and web services. Thus, the “big geospatial data management” issues start attracting attention. Among the big geospatial data issues, this research focuses on discovering distributed geospatial resources. As resources are scattered on the WWW, users cannot find resources of their interests efficiently. While the WWW has Web search engines addressing web resource discovery issues, we envision that the geospatial Web (i.e., GeoWeb) also requires GeoWeb search engines. To realize a GeoWeb search engine, one of the first steps is to proactively discover GeoWeb resources on the WWW. Hence, in this study, we propose the GeoWeb Crawler, an extensible Web crawling framework that can find various types of GeoWeb resources, such as Open Geospatial Consortium (OGC) web services, Keyhole Markup Language (KML) and Environmental Systems Research Institute, Inc (ESRI) Shapefiles. In addition, we apply the distributed computing concept to promote the performance of the GeoWeb Crawler. The result shows that for 10 targeted resources types, the GeoWeb Crawler discovered 7351 geospatial services and 194,003 datasets. As a result, the proposed GeoWeb Crawler framework is proven to be extensible and scalable to provide a comprehensive index of GeoWeb.

Description: This paper addresses how to manage planar spatial data using MongoDB, a popular NoSQL database characterized as a document-oriented, rich query language and high availability. The core idea is to flatten a hierarchical R-tree structure into a tabular MongoDB collection, during which R-tree nodes are represented as collection documents and R-tree pointers are expressed as document identifiers. By following this strategy, a storage schema to support R-tree-based create, read, update, and delete (CRUD) operations is designed and a module to manage planar spatial data by consuming and maintaining flattened R-tree structure is developed. The R-tree module is then seamlessly integrated into MongoDB, so that users could manipulate planar spatial data with existing command interfaces oriented to geodetic spatial data. The experimental evaluation, using real-world datasets with diverse coverage, types, and sizes, shows that planar spatial data can be effectively managed by MongoDB with our flattened R-tree and, therefore, the application extent of MongoDB will be greatly enlarged. Our work resulted in a MongoDB branch with R-tree support, which has been released on GitHub for open access.

Description: Conservation of forests outside protected areas is essential for maintaining forest connectivity, which largely depends on the effectiveness of local institutions. In this study, we use Landsat data to explore the relationship between vegetation structure and forest management institutions, in order to assess the efficacy of local institutions in management of forests outside protected areas. These forests form part of an important tiger corridor in Eastern Maharashtra, India. We assessed forest condition using 450 randomly placed 10 m radius circular plots in forest patches of villages with and without local institutions, to understand the impact of these institutions on forest vegetation. Tree density and species richness were significantly different between villages with and without local forest institutions, but there was no difference in tree biomass. We also found a significant difference in the relationship between tree density and NDVI between villages with and without local forest institutions. However, the relationship between species richness and NDVI did not differ significantly. The methods proposed by this study evaluate the status of forest management in a forest corridor using remotely sensed data and could be effectively used to identify the extent of vegetation health and management status.

Description: Zebra crossings provide guidance and warning to pedestrians and drivers, thereby playing an important role in traffic safety management. Most previous studies have focused on detecting zebra stripes but have not provided full information about the areas, which is critical to both driver assistance systems and guide systems for blind individuals. This paper presents a stepwise procedure for recognizing and reconstructing zebra crossings using mobile laser scanning data. First, we propose adaptive thresholding based on road surface partitioning to reduce the impact of intensity unevenness and improve the accuracy of road marking extraction. Then, dispersion degree filtering is used to reduce the noise. Finally, zebra stripes are recognized according to the rectangular feature and fixed size, which is followed by area reconstruction according to arrangement patterns. We test our method on three datasets captured by an Optech Lynx mobile mapping system. The total recognition rate of 90.91% demonstrates the effectiveness of the method.

Description: In recent years, with the reforms to the land use system and the development of urbanization in China, land price evaluation has tended towards marketization. Prices are determined by the government, the land transaction market and the public. It is necessary to propose higher standards to be used in the evaluation process. This paper presents an online land price evaluation approach for convenience in evaluation. In a network environment, taking advantage of the data services provided by various departments, we propose two models to assist in decision-making: (1) a geographic information system (GIS)- and fuzzy set-based location factor quantification model, which adopts dynamic data, rules and quantification measures (based on the road network) to dynamically quantify location factors, thus transforming fuzzy sets into appropriate values; and (2) a neartude-based transaction sample push model, which quantifies the similarity between a given land and other samples, thus providing a basis for decision-making by an appraiser. This approach is applied in Shenzhen to evaluate its ability to simplify the work of appraisers and make their decisions more intuitive and objective in a real case.

Description: The archiving of scientific data is a sophisticated mission in nearly all research projects. In this paper, we introduce a new online archive of atmospheric measurement data from the "High definition clouds and precipitation for advancing climate prediction" (HD(CP)2) research initiative. The project data archive is quality managed, easy to use, and is now open for other atmospheric research data. The archive’s creation was already taken into account during the HD(CP)2 project planning phase and the necessary resources were granted. The funding enabled the HD(CP)2 project to build a sound archive structure, which guarantees that the collected data are accessible for all researchers in the project and beyond.

Description: In this paper, an automatic approach for zebra crossing extraction and reconstruction from high-resolution aerial images is proposed. In the extraction procedure, zebra crossings are extracted by the JointBoost classifier based on GLCM (Gray Level Co-occurrence Matrix) features and 2D Gabor Features. In the reconstruction procedure, a geometric parameter model based on spatial repeatability relationships is globally fitted to reconstruct the geometric shape of zebra crossings. Additionally, a group of representative experiments is conducted to test the proposed method under interfered conditions, such as zebra crossings covered by pedestrians, shadows and color fading. Furthermore, the performance of the proposed extraction method is compared with the template matching method. Finally, the results show the validation of our proposed method, both in the extraction and reconstruction of zebra crossings.

Description: Simplification of three-dimensional (3D) buildings is critical to improve the efficiency of visualizing urban environments while ensuring realistic urban scenes. Moreover, it underpins the construction of multi-scale 3D city models (3DCMs) which could be applied to study various urban issues. In this paper, we design a generic yet effective approach for simplifying 3D buildings. Instead of relying on both semantic information and geometric information, our approach is based solely on geometric information as many 3D buildings still do not include semantic information. In addition, it provides an integrated means to treat 3D buildings with either sloped or flat roofs. The two case studies, one exploring simplification of individual 3D buildings at varying levels of complexity while the other, investigating the multi-scale simplification of a cityscape, show the effectiveness of our approach.

Description: In order discuss the impact of land consumption, it is first necessary to localize and quantify the extent of sealed surfaces. Since 2010, the monitoring of land use structures and developments in Germany has been provided by the Monitor of Settlement and Open Space Development at the Leibniz Institute of Ecological Urban and Regional Development (IÖR; IÖR Monitor), a scientific service operated by the Leibniz Institute of Ecological Urban and Regional Development. The IÖR Monitor includes an indicator for soil sealing for the years 2006, 2009 and 2012. Using this new source of data, it is possible for the first time to conduct quantitative studies at the level of Germany’s municipalities with the aim of documenting the extent of soil sealing as a form of spatial classification, as well as to investigate possible correlations with other influential factors. Here, we describe a comprehensive data inspection of soil sealing and potential influential factors. Structural interrelationships are identified under the application of classical and spatial regression methods.

Description: The Göttingen eResearch Alliance is presented as a case study for establishing institutional support for research data management within the context of the Göttingen Campus, a particular alliance of several research institutes at Göttingen. The cross-cutting, “horizontal” approach of the Göttingen eResearch Alliance, established by two research-oriented infrastructure providers, a research library and a computing and IT competence center, aims to coordinate Campus-led activities to establish sustainable and innovative services to support all phases of the research data life cycle. In this article, the core activities of the first phase aimed at developing a modular approach to provide support for research data management to researchers will be described. It closes with lessons learned and an outlook on future activities.

Description: The implementation of sustainable land policies is in need of monitoring methods that go beyond a mere description of the proportion values of land use classes. The annual statistical surface area report on actual land utilization (German: “Bodenfläche nach Art der tatsächlichen Nutzung”), published by the statistical offices of the German federal states and the federation, provides information on a set of pre-defined land use classes for municipalities, districts and federal states. Due to its surveying method of summing up usage information from cadastral registers, it is not possible to determine previous and subsequent usages of land parcels. Hence, it is hard to precisely indicate to what extent particular land use classes contribute to the settlement area increase. Nevertheless, this information is crucial to the understanding of land use change processes, which is needed for a subsequent identification of driving forces. To overcome this lack of information, a method for the spatial and quantitative determination of previous and subsequent land usages has been developed, implemented and tested. It is based on pre-processed land use data for different time slices, which are derived from authoritative geo-topographical base data. The developed method allows for the identification of land use changes considering small geometric shifts and changes in the underlying data model, which can be adaptively excluded from the balance.

Description: Land suitability analysis is employed to evaluate the appropriateness of land for a particular purpose whilst integrating both qualitative and quantitative inputs, which can be continuous in nature. However, in agricultural modelling there is often a disregard of this contiguous aspect. Therefore, some parametric procedures for suitability analysis compartmentalise units into defined membership classes. This imposition of crisp boundaries neglects the continuous formations found throughout nature and overlooks differences and inherent uncertainties found in the modelling. This research will compare two approaches to suitability analysis over three differing methods. The primary approach will use an Analytical Hierarchy Process (AHP), while the other approach will use a Fuzzy AHP over two methods; Fitted Fuzzy AHP and Nested Fuzzy AHP. Secondary to this, each method will be assessed into how it behaves in a climate change scenario to understand and highlight the role of uncertainties in model conceptualisation and structure. Outputs and comparisons between each method, in relation to area, proportion of membership classes and spatial representation, showed that fuzzy modelling techniques detailed a more robust and continuous output. In particular the Nested Fuzzy AHP was concluded to be more pertinent, as it incorporated complex modelling techniques, as well as the initial AHP framework. Through this comparison and assessment of model behaviour, an evaluation of each methods predictive capacity and relevance for decision-making purposes in agricultural applications is gained.

Description: The problem of heading drift error using only low cost Micro-Electro-Mechanical (MEMS) Inertial-Measurement-Unit (IMU) has not been well solved. In this paper, a heading estimation method with real-time compensation based on Kalman filter has been proposed, abbreviated as KHD. For the KHD method, a unified heading error model is established for various predictable errors in magnetic compass for pedestrian navigation, and an effective method for solving the model parameters is proposed in the indoor environment with regular structure. In addition, error model parameters are solved by Kalman filtering algorithm with building geometry information in order to achieve real-time heading compensation. The experimental results show that the KHD method can not only effectively correct the original heading information, but also effectively inhibit the accumulation effect of positioning errors. The performance observed in a field experiment performed on the fourth floor of the School of Environmental Science and Spatial Informatics (SESSI) building on the China University of Mining and Technology (CUMT) campus confirms that apply KHD method to PDR(Pedestrian Dead Reckoning) algorithm can reliably achieve meter-level positioning using a low cost MEMS IMU only.

Description: Multitemporal biodiversity data on a forest ecosystem can provide useful information about the evolution of biodiversity in a territory. The present study describes the recovery of an archive used to determine the main Schmid’s vegetation belts in Trento Province, Italy. The archive covers 20 years, from the 1970s to the 1990s. During the FORCING project (an Italian acronym for Cingoli Forestali, i.e., forest belts), a comprehensive process of database recovering was executed, and missing data were digitized from historical maps, preserving paper-based maps and documents. All of the maps of 16 forest districts, and the related 8000 detected transects, have been georeferenced to make the whole database spatially explicit and to evaluate the possibility of performing comparative samplings on up-to-date datasets. The floristic raw data (approximately 200,000 specific identifications, including frequency indices) still retain an important and irreplaceable information value. The data can now be browsed via a web-GIS. We provide here a set of examples of the use of this type of data, and we highlight the potential and the limits of the specific dataset and of the historical database, in general.

Description: A Bayesian random effects modeling approach was used to examine the influence of neighborhood characteristics on burglary risks in Jianghan District, Wuhan, China. This random effects model is essentially spatial; a spatially structured random effects term and an unstructured random effects term are added to the traditional non-spatial Poisson regression model. Based on social disorganization and routine activity theories, five covariates extracted from the available data at the neighborhood level were used in the modeling. Three regression models were fitted and compared by the deviance information criterion to identify which model best fit our data. A comparison of the results from the three models indicates that the Bayesian random effects model is superior to the non-spatial models in fitting the data and estimating regression coefficients. Our results also show that neighborhoods with above average bar density and department store density have higher burglary risks. Neighborhood-specific burglary risks and posterior probabilities of neighborhoods having a burglary risk greater than 1.0 were mapped, indicating the neighborhoods that should warrant more attention and be prioritized for crime intervention and reduction. Implications and limitations of the study are discussed in our concluding section.

Description: Decision-makers in the fields of urban and regional planning in Germany face new challenges. High rates of urban sprawl need to be reduced by increased inner-urban development while settlements have to adapt to climate change and contribute to the reduction of greenhouse gas emissions at the same time. In this study, we analyze conflicts in the management of urban areas and develop integrated sustainable land use strategies for Germany. The spatial explicit land use change model Land Use Scanner is used to simulate alternative scenarios of land use change for Germany for 2030. A multi-criteria analysis is set up based on these scenarios and based on a set of indicators. They are used to measure whether the mitigation and adaptation objectives can be achieved and to uncover conflicts between these aims. The results show that the built-up and transport area development can be influenced both in terms of magnitude and spatial distribution to contribute to climate change mitigation and adaptation. Strengthening the inner-urban development is particularly effective in terms of reducing built-up and transport area development. It is possible to reduce built-up and transport area development to approximately 30 ha per day in 2030, which matches the sustainability objective of the German Federal Government for the year 2020. In the case of adaptation to climate change, the inclusion of extreme flood events in the context of spatial planning requirements may contribute to a reduction of the damage potential.

Description: With the rise of new technologies, citizens can contribute to scientific research via Web 2.0 applications for collecting and distributing geospatial data. Integrating local knowledge, personal experience and up-to-date geoinformation indicates a promising approach for the theoretical framework and the methods of natural hazard analysis. Our systematic literature review aims at identifying current research and directions for future research in terms of Volunteered Geographic Information (VGI) within natural hazard analysis. Focusing on both the preparedness and mitigation phase results in eleven articles from two literature databases. A qualitative analysis for in-depth information extraction reveals auspicious approaches regarding community engagement and data fusion, but also important research gaps. Mainly based in Europe and North America, the analysed studies deal primarily with floods and forest fires, applying geodata collected by trained citizens who are improving their knowledge and making their own interpretations. Yet, there is still a lack of common scientific terms and concepts. Future research can use these findings for the adaptation of scientific models of natural hazard analysis in order to enable the fusion of data from technical sensors and VGI. The development of such general methods shall contribute to establishing the user integration into various contexts, such as natural hazard analysis.

Description: Rapid urbanization has caused many environmental problems, such as the heat island effect, intensifying air pollution, pollution from runoff, loss of wildlife habitat, etc. Accurate evaluations of these problems demand an accurate delineation of the spatial extent of the urban fringe. Conceptual and analytical ambiguity of the urban fringe and a general lack of consensus among researchers have made its measurement very difficult. This study reports a compound and reliable method to delineate the urban fringe area using a case study. Based on the 'fringe effect' theory in landscape ecology, the existing land cover information entropy model for defining the urban fringe is renewed by incorporating scale theory, cartography and urban geography theory. Results show that the urban fringe area of Guangzhou and Foshan metropolitan area covers an area of 2031 km2, and it occupies over 31% of the total study area. Result evaluation by industry structure data shows satisfactory correspondence with different land cover types. This paper reports the method and outcome of an attempt to provide an objective, repeatable and generally applicable method for mapping its spatial extent from remote sensing imageries, and could be beneficial to relevant urban studies and urban fringe management projects.

Description: The timely detection of gold foil damage in gold-overlaid stone carvings and the associated maintenance of these relics pose several challenges to both the research and heritage protection communities internationally. This paper presents a new method for detecting gold foil damage by making use of multi-temporal 3D LiDAR point clouds. By analyzing the errors involved in the detection process, a formula is developed for calculation of the damage detection threshold. An improved division method for the linear octree that only allocates memory to the non-blank nodes, is proposed, which improves storage and retrieval efficiency for the point clouds. Meanwhile, the damage-occurrence regions are determined according to Hausdorff distances. Using a triangular mesh, damaged regions can be identified and measured in order to determine the relic’s total damaged area. Results demonstrate that this method can effectively detect gold foil damage in stone carvings. The identified surface area of damaged regions can provide the information needed for subsequent restoration and protection of relics of this type.

Description: The recent urban growth of Florence was mainly oriented northward, thus determining the urbanization of the flatland and the inclusion within a unique conurbation of a number of pre-existing urban nuclei. Over time, the congestion of the inner core has caused more and more prominent activities to shift towards this developing area, which is today one of the most attractive parts of the whole settlement, counterbalancing the representativeness and the touristic attractiveness of the historic center of Florence. This paper is concerned with the use of space syntax in order to reconstruct the genesis of the configurational geography of Florence. Configurational values at different dates will be cross-referenced with vehicular traffic data, so as to pinpoint the actual inclusion of the motorway A1, touching Florence on its western side, within the urban grid of Florence and its influence in the distribution of local traffic flows. Aside from this case study, this method can be extended to the general issue of the management of motorways in metropolitan areas. More in general, this approach is proposed as a suitable tool for interconnecting spatial issues and traffic questions, so as to concur in bridging the gap between urban design, focused on the morphologic features of blocks and buildings, and transport analysis, strictly concerned with the distribution of movement flows on the street network.

Description: Human development has far-reaching impacts on the surface of the globe. The transformation of natural land cover occurs in different forms, and urban growth is one of the most eminent transformative processes. We analyze global land cover data and extract cities as defined by maximally connected urban clusters. The analysis of the city size distribution for all cities on the globe confirms Zipf’s law. Moreover, by investigating the percolation properties of the clustering of urban areas we assess the closeness to criticality for various countries. At the critical thresholds, the urban land cover of the countries undergoes a transition from separated clusters to a gigantic component on the country scale. We study the Zipf-exponents as a function of the closeness to percolation and find a systematic dependence, which could be the reason for deviating exponents reported in the literature. Moreover, we investigate the average size of the clusters as a function of the proximity to percolation and find country specific behavior. By relating the standard deviation and the average of cluster sizes—analogous to Taylor’s law—we suggest an alternative way to identify the percolation transition. We calculate spatial correlations of the urban land cover and find long-range correlations. Finally, by relating the areas of cities with population figures we address the global aspect of the allometry of cities, finding an exponent δ ≈ 0.85, i.e., large cities have lower densities.

Description: Accuracy is increasingly recognized as an important dimension in geospatial information and analyses. A strategy well suited for map users who usually have limited information about map lineages is proposed for location-specific characterization of accuracy in land cover change maps. Logistic regression is used to predict the probabilities of correct change categorization based on local patterns of map classes in the focal three by three pixel neighborhood centered at individual pixels being analyzed, while kriging is performed to make corrections to regression predictions based on regression residuals at sample locations. To promote uncertainty-informed accuracy characterization and to facilitate adaptive sampling of validation data, standard errors in both regression predictions and kriging interpolation are quantified to derive error margins in the aforementioned accuracy predictions. It was found that the integration of logistic regression and kriging leads to more accurate predictions of local accuracies through proper handling of spatially-correlated binary data representing pixel-specific (in)correct classifications than kriging or logistic regression alone. Secondly, it was confirmed that pixel-specific class labels, focal dominances and focal class occurrences are significant covariates for regression predictions at individual pixels. Lastly, error measures computed of accuracy predictions can be used for adaptively and progressively locating samples to enhance sampling efficiency and to improve predictions. The proposed methods may be applied for characterizing the local accuracy of categorical maps concerned in spatial applications, either input or output.

Description: The Internet of Things (IoT) opens up tremendous opportunities to provide location-based applications. However, despite the services around a user being physically adjacent, common IoT platforms use a centralized structure, like a cloud-computing architecture, which transfers large amounts of data to a central server. This raises problems, such as traffic concentration, long service latency, and high communication cost. In this paper, we propose a physical distance-based asynchronous messaging platform that specializes in processing personalized data and location-based messages. The proposed system disperses traffic using a location-based message-delivery protocol, and has high stability.

Description: In the recent big data era, massive spatial related data are continuously generated and scrambled from various sources. Acquiring accurate geographic information is also urgently demanded. How to accurately retrieve desired geographic information has become the prominent issue, needing to be resolved in high priority. The key technologies in geographic information retrieval are modeling document footprints and ranking documents based on their similarity evaluation. The traditional spatial similarity evaluation methods are mainly performed using a MBR (Minimum Bounding Rectangle) footprint model. However, due to its nature of simplification and roughness, the results of traditional methods tend to be isotropic and space-redundant. In this paper, a new model that constructs the footprints in the form of point-sets is presented. The point-set-based footprint coincides the nature of place names in web pages, so it is redundancy-free, consistent, accurate, and anisotropic to describe the spatial extents of documents, and can handle multi-scale geographic information. The corresponding spatial ranking method is also presented based on the point-set-based model. The new similarity evaluation algorithm of this method firstly measures multiple distances for the spatial proximity across different scales, and then combines the frequency of place names to improve the accuracy and precision. The experimental results show that the proposed method outperforms the traditional methods with higher accuracies under different searching scenarios.

Description: Rapid urbanization has caused numerous problems, and the urban spatial structure has been a hot topic in sustainable development management. Urban spatial structure is affected by a series of factors. Thus, the research model should synthetically consider the spatial and non-spatial relationship of every element. Here, we propose an extended Voronoi diagram for exploring the urban land spatial pattern. In essence, we first used a principal component analysis method to construct attribute evaluation indicators and obtained the attribute distance for each indicator. Second, we integrated spatial and attribute distances to extend the comparison distance for Voronoi diagrams, and then, we constructed the Voronoi aggregative homogeneous map of the study area. Finally, we make a spatial autocorrelation analysis by using GeoDA and SPSS software. Results show that: (1) the residential land cover aggregation is not significant, but spatial diffusion is obvious; (2) the commercial land cover aggregation is considerable; and (3) the spatial agglomeration degree of the industrial land cover is increased and mainly located in urban fringes. According to the neo-Marxist theory, we briefly analyzed the driving forces for shaping the urban spatial structure. To summarize, our approach yields important insights into the urban spatial structure characterized by attribute similarity with geospatial proximity, which contributes to a better understanding of the urban growth mechanism. In addition, it explicitly identifies ongoing urban transformations, potentially supporting the planning for sustainable urban land use and protection.

Description: Current moving-object database (MOD) systems focus on management of movement data, but pay less attention to modelling social relationships between moving objects and spatial-temporal trajectories in an integrated manner. This paper combines moving-object database and social network systems and presents a novel data model called Geo-Social-Moving (GSM) that enables the unified management of trajectories, underlying geographical space and social relationships for mass moving objects. A bulk of user-defined data types and corresponding operators are also proposed to facilitate geo-social queries on moving objects. An implementation framework for the GSM model is proposed, and a prototype system based on native Neo4J is then developed with two real-world data sets from the location-based social network systems. Compared with solutions based on traditional extended relational database management systems characterized by time-consuming table join operations, the proposed GSM model characterized by graph traversal is argued to be more powerful in representing mass moving objects with social relationships, and more efficient and stable for geo-social querying.

Description: The relationship between vegetation, transportation networks, and crime has been under debate. Vegetation has been positively correlated with fear of crime; however, the actual correlation between vegetation and occurrences of crime is uncertain. Transportation networks have also been connected with crime occurrence but their impact on crime tends to vary over different circumstances. By conducting spatial analyses, this study explores the associations between crime and vegetation as well as transportation networks in Kitchener-Waterloo. Further, geographically weighted regression modeling and a dummy urban variable representing the urban center/other urban areas were employed to explore the associations across an urban central-peripheral gradient. Associations were analyzed for crimes against persons and crimes against property for four specific crime types (assaults, vehicle theft, sex offences, and drugs). Results suggest that vegetation has a reverse association with crimes against persons and crimes against property while transportation networks have a positive relationship with these two types of crime. Additionally, vegetation can be a deterrent to vehicle theft crime and drugs, while transportation networks can be a facilitator of drug-related crimes. Besides, these two associations appear stronger in the urban center compared to the urban periphery.

Description: To improve the search ability of biogeography-based optimization (BBO), this work proposed an improved biogeography-based optimization based on Affinity Propagation. We introduced the Memetic framework to the BBO algorithm, and used the simulated annealing algorithm as the local search strategy. MBBO enhanced the exploration with the Affinity Propagation strategy to improve the transfer operation of the BBO algorithm. In this work, the MBBO algorithm was applied to IEEE Congress on Evolutionary Computation (CEC) 2015 benchmarks optimization problems to conduct analytic comparison with the first three winners of the CEC 2015 competition. The results show that the MBBO algorithm enhances the exploration, exploitation, convergence speed and solution accuracy and can emerge as the best solution-providing algorithm among the competing algorithms.

Description: We propose a new segmentation and grouping framework for road map inference from GPS traces. We first present a progressive Density-Based Spatial Clustering of Application with Noise (DBSCAN) algorithm with an orientation constraint to partition the whole point set of the traces into clusters that represent road segments. A new point cluster grouping algorithm, according to the topological relationship and spatial proximity of the point clusters to recover the road network, is then developed. After generating the point clusters, the robust Locally-Weighted Scatterplot Smooth (Lowess) method is used to extract their centerlines. We then propose to build the topological relationship of the centerlines by a Hidden Markov Model (HMM)-based map matching algorithm; and to assess whether the spatial proximity between point clusters by assuming the distances from the points to the centerline comply with a Gaussian distribution. Finally, the point clusters are grouped according to their topological relationship and spatial proximity to form strokes for recovering the road map. Experimental results show that our algorithm is robust to noise and varied sampling rates. The generated road maps show high geometric accuracy.

Description: The Receiver Operating Characteristic (ROC) is widely used for assessing the performance of classification algorithms. In GIScience, ROC has been applied to assess models aimed at predicting events, such as land use/cover change (LUCC), species distribution and disease risk. However, GIS software packages offer few statistical tests and guidance tools for ROC analysis and interpretation. This paper presents a suite of GIS tools designed to facilitate ROC curve analysis for GIS users by applying proper statistical tests and analysis procedures. The tools are freely available as models and submodels of Dinamica EGO freeware. The tools give the ROC curve, the area under the curve (AUC), partial AUC, lower and upper AUCs, the confidence interval of AUC, the density of event in probability bins and tests to evaluate the difference between the AUCs of two models. We present first the procedures and statistical tests implemented in Dinamica EGO, then the application of the tools to assess LUCC and species distribution models. Finally, we interpret and discuss the ROC-related statistics resulting from various case studies.

Description: Web service is a technological solution for software interoperability that supports the seamless integration of diverse applications. In the vision of web service architecture, web services are described by the Web Service Description Language (WSDL), discovered through Universal Description, Discovery and Integration (UDDI) and communicate by the Simple Object Access Protocol (SOAP). Such a divination has never been fully accomplished yet. Although it was criticized that WSDL only has a syntactic definition of web services, but was not semantic, prior initiatives in semantic web services did not establish a correct methodology to resolve the problem. This paper examines the distinction and relationship between the syntactic and semantic definitions for web services that characterize different purposes in service computation. Further, this paper proposes that the semantics of web service are neutral and independent from the service interface definition, data types and platform. Such a conclusion can be a universal law in software engineering and service computing. Several use cases in the GIScience application are examined in this paper, while the formalization of geospatial services needs to be constructed by the GIScience community towards a comprehensive ontology of the conceptual definitions and relationships for geospatial computation. Advancements in semantic web services research will happen in domain science applications.

Description: The efficacy of integrating open access geospatial data to produce habitat suitability maps for the corn bunting (Miliaria calandra) was investigated. Landsat Enhanced Thematic Mapper Plus (ETM+), Shuttle Radar Topography Mission (SRTM) and Corine (Coordination of Information on the Environment) land cover data for the year 2000 (CLC2000) were processed to extract explanatory variables and divided into three sets; Satellite (ETM+, SRTM), CLC2000 and Combined (CLC2000 + Satellite). Presence-absence data for M. calandra, collected during structured surveys for the Catalan Breeding Bird Atlas, were provided by the Catalan Ornithological Institute. The dataset was partitioned into an equal number of presence and absence points by dividing it into five groups, each composed of 88 randomly selected presence points to match the number of absences. A logistic regression model was then built for each group. Models were evaluated using area under the curve (AUC) of the receiver operating characteristic (ROC). Results of the five groups were averaged to produce mean Satellite, CLC2000 and Combined models. The mean AUC values were 0.69, 0.81 and 0.90 for the CLC2000, Satellite and the Combined model, respectively. The probability of M. calandra presence had the strongest positive correlation with land surface temperature, modified soil adjusted vegetation index, coefficient of variation for ETM+ band 5 and the fraction of non-irrigated arable land.

Description: There are many situations in which it is desirable to use a Local Positioning System (LPS), which constitutes a complete and independent unit, offers high accuracy and in addition is economical to realize. This paper describes the LPS LOSNUS (Localization of Sensor Nodes by Ultra Sound). LOSNUS is a complete and independent LPS where the same system can be used for localization and calibration. Primarily designed for locating numerous quasi-static devices, special care of system construction has taken on costly factors, especially in the construction of the infrastructure and of sensor nodes where locating can be realized with minimal additional hardware costs. LOSNUS enables a calibration process without the need of additional expensive tools and/or laborious time in order to get accurate positions of transmitters. As a result, LOSNUS delivers high locating accuracy at medium update rates, and in case of sufficient number of transmitters can also tolerate single failures in the Time of Arrival (ToA) measurement, allowing arbitrary failure modes. In this article, the system is presented starting from design, realization and algorithms of localization and calibration. Finally, new measurement results are showing the high accuracy of localization based on a discussion of the applied uncertainty description.

Description: This study analyzed more than 50 years of land cover and land use changes in the 260 km2 Koga catchment in North Western Ethiopia. The data used includes 1:50,000 scale aerial photographs, Landsat MSS, TM and ETM images, and ASTER images together with ground truth data collected through field surveys and community elders’ interviews. Aerial photographs have high spatial resolution but provide lower spectral resolution than satellite data. While most land use/cover change studies compare changes from different spatial scales, this study applied land use/cover classification techniques to bring the data to a relatively similar scale. The data revealed that woody vegetation decreased from 5,576 ha to 3,012 ha from the 1950s to 2010. Most of the deforestation took place between the 1970s and 1980s, but there is an increasing trend since then. No significant changes were observed in the area used for agriculture that comprises the pastures and crop fields since the 1950s, while there is an enormous increase in the area used for settlement, due to a tremendous increase in population from one point in time to another. The bare lands that used to exist in previous years were found to be totally covered with other land cover/use classes and no bare lands were observed in the study area in the year 2010. Population pressure and land use policies were found to be reasons for the changes in land use/cover while soil degradation, decrease in the indigenous woody vegetation and erosion were the observed consequences of the land use/cover changes.

Description: Recent profound changes have been observed in the Arctic environment, including record low sea ice extents and high latitude greening. Studying the Arctic and how it is changing is an important element of climate change science. The Tundra, an ecoregion of the Arctic, is directly related to climate change due to its effects on the snow ice feedback mechanism and greenhouse gas cycling. Like all ecoregions, the Tundra border is shifting, yet studies and policies require clear delineation of boundaries. There are many options for ecoregion classification systems, as well as resources for creating custom maps. To help decision makers identify the best classification system possible, we present a review of North American Tundra ecoregion delineations and further explore the methodologies, purposes, limitations, and physical properties of five common ecoregion classification systems. We quantitatively compare the corresponding maps by area using a geographic information system.

Description: As open source volunteered geographic information continues to gain popularity, the user community and data contributions are expected to grow, e.g., CloudMade, Apple, and Ushahidi now provide OpenStreetMap© (OSM) as a base layer for some of their mapping applications. This, coupled with the lack of cartographic standards and the expectation to one day be able to use this vector data for more geopositionally sensitive applications, like GPS navigation, leaves potential users and researchers to question the accuracy of the database. This research takes a photogrammetric approach to determining the positional accuracy of OSM road features using stereo imagery and a vector adjustment model. The method applies rigorous analytical measurement principles to compute accurate real world geolocations of OSM road vectors. The proposed approach was tested on several urban gridded city streets from the OSM database with the results showing that the post adjusted shape points improved positionally by 86%. Furthermore, the vector adjustment was able to recover 95% of the actual positional displacement present in the database. To demonstrate a practical application, a head-to-head positional accuracy assessment between OSM, the USGS National Map (TNM), and United States Census Bureau’s Topologically Integrated Geographic Encoding Referencing (TIGER) 2007 roads was conducted.

Description: In this work we present preliminary results regarding a proof-of-concept project which aims to provide tools for mapping the amount of solar radiation reaching surfaces of objects, accounting for obstructions between objects themselves. The implementation uses the NASA World Wind development platform (NASA WW) to model the different physical phenomena that participate in the process, from the calculation of the Sun’s position relative to the area that is being considered, to the interaction between atmosphere and solid obstructions, e.g., terrain or buildings. A more complete understanding of the distribution of energy from the Sun illuminating elements on the Earth’s surface adds value to applications ranging from planning the renewable energy potential of an area to ecological analyses.

Description: The relevance of geographic information to mobile users must be evaluated by taking into account the usage context. This paper assumes that emerging Location-based Social Networks (LBSNs) contain contextual information rich enough to be used in order to contextualize such an evaluation process. This assumption is demonstrated through an exploratory analysis of a Foursquare check-in dataset, which reveals the impacts of two contextual factors—temporal and spatial—on mobility patterns. This paper then proposes an approach that may be used to contextualize the evaluation of geographic information’s relevance. The proposed algorithm links a priori relevance to the contextualized relevance using the hidden impacts of contextual factors. Improved performance from the experiments carried out confirms the validity of the proposed approach, as well as the benefits of utilizing contextual information within the relevance evaluation process.

Description: In this paper we explore relationships between bird species richness and environmental factors in New York State, focusing particularly on how spatial scale, autocorrelation and nonstationarity affect these relationships. We used spatial statistics, Getis-Ord Gi*(d), to investigate how spatial scale affects the measurement of richness “hot-spots” and “cold-spots” (clusters of high and low species richness, respectively) and geographically weighted regression (GWR) to explore scale dependencies and nonstationarity in the relationships between richness and environmental variables such as climate and plant productivity. Finally, we introduce a geovisualization approach to show how these relationships are affected by spatial scale in order to understand the complex spatial patterns of species richness.

Description: As a consequence of increasing safety concerns, camera surveillance has been widely adopted as a way to monitor public spaces. One of the major challenges of camera surveillance is to design an optimal method for camera network placement in order to ensure the greater possible coverage. In addition, this method must consider the landscape of the monitored environment to take into account the existing objects that may influence the deployment of such a network. In this paper, a new Voronoi-based 3D GIS oriented approach named “HybVOR” is proposed for surveillance camera network placement. The “HybVOR” approach aims to achieve a coverage near 100% through three main phases. First, a Voronoi Diagram from buildings’ footprints is generated and cameras are placed on the Voronoi Edges. Second, the level of coverage is assessed by calculating a viewshed based on a raster Digital Surface Model of the region of interest. Finally, the visibility of the main buildings’ entrances is evaluated based on a 3D vector model that contains these features. The effectiveness of the “HybVOR” approach is demonstrated through a case study that corresponds to an area of interest in Jeddah Seaport in the Kingdom of Saudi Arabia.

Description: People with disabilities face many obstacles in everyday outdoor travels. One of the most notable obstacles is steep slope on sidewalk segments. Current navigation systems/services do not all support map databases with slope attributes and cannot calculate sidewalk slope in real time. In this paper, we present a technique for calculating slopes of sidewalk segments by image data and predict the most suitable route for each individual user through integration with GPS trajectory. In our technique we make use of GPS trajectory data, to identify the sidewalk segment on which the traveler will most probably pass, and images of the identified sidewalk segment. Through edge detection techniques we detect edges of objects, such as buildings, billboards, and walls, in the background. Slope of the segment is then calculated by comparing its line representation in the map with the detected edges. Our experiment result indicates effective calculation of sidewalk slopes.

Description: The house crow (Corvus splendens), a native of the Indian subcontinent, has shown a rapid expansion of habitat range across Eastern Africa, the Arabian Peninsula, Europe and Asia. It is an adaptable, gregarious commensal bird which is regarded globally as an important pest species due to its impacts on livestock, agricultural and horticultural crops and indigenous fauna and as a fecal contaminator of human environments and water resources. Two Maxent (v3.3.3k) models (A) with presence data in Australia and (B) with simulated entry data locations in New Zealand) and a third ArcGIS model (C) with environmental and social layers) are used to determine an overall suitability index and establish a niche-based model of the potential spatial distribution for C. splendens within New Zealand. The results show that New Zealand, particularly the northern regions of North Island, has suitable environments for the establishment of the house crow. In order of suitability Model B showed highest potential land area suitability (31.84%) followed by Model A (13.79%) and Model C (10.89%). The potential for further expansion of this bird’s invasive range is high and, if New Zealand is invaded, impacts are likely to be significant.

Description: This article reviews the range of delivery platforms that have been developed for the PySAL open source Python library for spatial analysis. This includes traditional desktop software (with a graphical user interface, command line or embedded in a computational notebook), open spatial analytics middleware, and web, cloud and distributed open geospatial analytics for decision support. A common thread throughout the discussion is the emphasis on openness, interoperability, and provenance management in a scientific workflow. The code base of the PySAL library provides the common computing framework underlying all delivery mechanisms.

Description: Readability is a major issue with all maps. In this study, we evaluated whether we can predict map readability using analytical measures, both single measures and composites of measures. A user test was conducted regarding the perceived readability of a number of test map samples. Evaluations were then performed to determine how well single measures and composites of measures could describe the map readability. The evaluation of single measures showed that the amount of information was most important, followed by the spatial distribution of information. The measures of object complexity and graphical resolution were not useful for explaining the map readability of our test data. The evaluations of composites of measures included three methods: threshold evaluation, multiple linear regression and support vector machine. We found that the use of composites of measures was better for describing map readability than single measures, but we could not identify any major differences in the results of the three composite methods. The results of this study can be used to recommend readability measures for triggering and controlling the map generalization process of online maps.

Description: By applying visual analytics techniques to vehicle traffic data, we found a way to visualize and study the relationships between the traffic intensity and movement speed on links of a spatially abstracted transportation network. We observed that the traffic intensities and speeds in an abstracted network are interrelated in the same way as they are in a detailed street network at the level of street segments. We developed interactive visual interfaces that support representing these interdependencies by mathematical models. To test the possibility of utilizing them for performing traffic simulations on the basis of abstracted transportation networks, we devised a prototypical simulation algorithm employing these dependency models. The algorithm is embedded in an interactive visual environment for defining traffic scenarios, running simulations, and exploring their results. Our research demonstrates a principal possibility of performing traffic simulations on the basis of spatially abstracted transportation networks using dependency models derived from real traffic data. This possibility needs to be comprehensively investigated and tested in collaboration with transportation domain specialists.

Description: Thanks to the increasing popularity of location-based social networks, a large amount of user-generated geo-referenced check-in data is now available, and such check-in data is becoming a new data source in the study of mobility and travel. Conventionally, spatial interactions between places were measured based on the trips made between them. This paper empirically investigates the use of social media data (i.e., Foursquare data) to study the “locality” of such intra-urban spatial interactions in New York City, and specifically: (i) the level of “locality” of spatial interactions; (ii) the impacts of personal characteristics on “locality” of spatial interaction and finally; (iii) the heterogeneity in spatial distribution of “local” interactions. The results of this study indicate that: (1) spatial interactions show a high degree of locality; (2) gender does not have a considerable impact on the locality of spatial interactions and finally; (3) “local” interactions likely cluster in some places within the research city.

Description: The 3 August 2014 Ludian earthquake with a moment magnitude scale (Mw) of 6.1 induced widespread landslides in the Ludian County and its vicinity. This paper presents a preliminary analysis of the distribution patterns and characteristics of these co-seismic landslides. In total, 1826 landslides with a total area of 19.12 km2 triggered by the 3 August 2014 Ludian earthquake were visually interpreted using high-resolution aerial photos and Landsat-8 images. The sizes of the landslides were, in general, much smaller than those triggered by the 2008 Wenchuan earthquake. The main types of landslides were rock falls and shallow, disrupted landslides from steep slopes. These landslides were unevenly distributed within the study area and concentrated within an elliptical area with a 25-km NW–SE striking long axis and a 15-km NW–SE striking short axis. Three indexes including landslides number (LN), landslide area ratio (LAR), and landslide density (LD) were employed to analyze the relation between the landslide distribution and several factors, including lithology, elevation, slope, aspect, distance to epicenter and distance to the active fault. The results show that slopes consisting of deeply weathered and fractured sandstones and mudstones were the more susceptible to co-seismic landslides. The elevation range of high landslide susceptibility was between 900–1300 m and 1800–2000 m. There was a generally positive correlation between co-seismic landslides and slope angle, until a maximum for the slope class 40°–50°. The co-seismic landslides occurred preferably on Southeast (SE), South (S) and Southwest (SW) oriented slopes. Results also show that the landslide concentration tends to decrease with distance from the surface projection of the epicenter rather than the seismogenic fault, and the highest landslide concentration is located within a 5–6 km distance of the seismogenic fault. Regarding the epicenter, the largest landslide clusters were found on the SE, northeast by east (NEE) and nearly West (W) of the epicenter. In addition, we also suggest that statistical results of slope gradients of landslides might imply a threshold topography of the study area within a tectonically active background. By analogy with other events, the statistical results of landslides aspects also imply the seismogenic fault of the Ludian earthquake might have been the Northwest (NW)-trending fault, which is consistent with other studies.

Description: Since important national and international funders of research projects require statements on the long-term accessibility of research results, many new solutions appeared to fulfil these demands. The solutions are implemented on various scopes, starting from specific solutions for one research group up to solutions with a national focus (i.e., the RADAR project). While portals for globally standardized research data (e.g., climate data) are available, there is currently no provision for the large amount of data resulting from specialized research in individual research foci, the so called long-tail of sciences. In this article we describe the considerations regarding the implementation of a local research data repository for the Collaborative Research Centre (CRC) 840. The main focus will be on the examination of requirements for, and an agenda of, a possible technical implementation. Requirements were derived from a more theoretical examination of similar projects and relevant literature, diverse discussions with researchers and project leaders, by analysis of existing publication data, and finally the prototypical implementation with refining iterations. Notably, the discussions with the researchers lead to new features going beyond the challenges of the mere long-term preservation of research data. Besides the need for an infrastructure that permits long-term preservation and retrieval of research data, our system will allow the reconstruction of the complete provenance of published research results. This requirement is a serious diversification of the problem, because it creates the need to qualify additional transformation data, describing the transformation process from primary research data to research results.

Description: Floating car data (FCD) recorded with the Global Positioning System (GPS) are an important data source for traffic research. However, FCD are subject to error, which can relate either to the accuracy of the recordings (measurement error) or to the temporal rate at which the data are sampled (interpolation error). Both errors affect movement parameters derived from the FCD, such as speed or direction, and consequently influence conclusions drawn about the movement. In this paper we combined recent findings about the autocorrelation of GPS measurement error and well-established findings from random walk theory to analyse a set of real-world FCD. First, we showed that the measurement error in the FCD was affected by positive autocorrelation. We explained why this is a quality measure of the data. Second, we evaluated four metrics to assess the influence of interpolation error. We found that interpolation error strongly affects the correct interpretation of the car’s dynamics (speed, direction), whereas its impact on the path (travelled distance, spatial location) was moderate. Based on these results we gave recommendations for recording of FCD using the GPS. Our recommendations only concern time-based sampling, change-based, location-based or event-based sampling are not discussed. The sampling approach minimizes the effects of error on movement parameters while avoiding the collection of redundant information. This is crucial for obtaining reliable results from FCD.

Description: The key focus of this paper is to establish a procedure that combines the use of Geographical Information Systems (GIS) and remote sensing in order to achieve simulation and modeling of the landscape impact caused by construction. The procedure should be easily and inexpensively developed. With the aid of 3D virtual reconstruction and visualization, this paper proposes that the technologies of remote sensing and GIS can be applied to the landscape for post-urbanization environmental restoration. The goal is to create a rural zone in an urban development sector that integrates the residential areas and local infrastructure into the surrounding natural environment in order to measure the changes to the preliminary urban design. The units of the landscape are determined by means of two cartographic methods: (1) indirect, using the components of the landscape; and (2) direct methods, using the landscape’s elements. The visual basins are calculated for the most transited by the population points, while establishing the zones that present major impacts for the urbanization of their landscape. Based on this, the different construction types are distributed (one-family houses, blocks of houses, etc.), selecting the types of plant masses either with ornamentals or integration depending on the zone; integrating water channels, creating a water channel in recirculation and green spaces and leisure time facilities. The techniques of remote sensing and GIS allow for the visualization and modeling of the urbanization in 3D, simulating the virtual reality of the infrastructure as well as the actions that need to be taken for restoration, thereby providing at a low cost an understanding of landscape integration before it takes place.

Description: Trajectories, representing the movements of objects in the real world, carry significant stop/move semantics. The detection of trajectory stops poses a critical problem in the study of moving objects and becomes even more challenging due to the inevitable noise recorded along with true data. To extract stops with a variety of shapes and sizes from single trajectories with noise, this paper presents a sequence oriented clustering approach, in which noise points within the sequence of a stop can be identified and classified as a part of the stop. In our method, two key concepts are first introduced: (1) a core sequence that defines sequence density based not only on proximity in space but also continuity in time as well as the duration over time; and (2) an Eps-reachability sequence that aggregates core sequences that overlap or meet over time. Then, three criteria are presented to merge Eps-reachability sequences interrupted by noise. Further, an algorithm, called SOC (Sequence Oriented Clustering), is developed to automatically extract stops from a single trajectory. In addition, a reachability graph is designed that visually illustrates the spatio-temporal clustering structure and levels of a trajectory. Finally, the proposed algorithm is evaluated against two baseline methods through extensive experiments based on real world trajectories, some with serious noise, and the results show that our approach is fairly effective in recognizing trajectory stops.

Description: The aim of the RADAR (Research Data Repository) project is to set up and establish an infrastructure that facilitates research data management: the infrastructure will allow researchers to store, manage, annotate, cite, curate, search and find scientific data in a digital platform available at any time that can be used by multiple (specialized) disciplines. While appropriate and innovative preservation strategies and systems are in place for the big data communities (e.g., environmental sciences, space, and climate), the stewardship for many other disciplines, often called the “long tail research domains”, is uncertain. Funded by the German Research Foundation (DFG), the RADAR collaboration project develops a service oriented infrastructure for the preservation, publication and traceability of (independent) research data. The key aspect of RADAR is the implementation of a two-stage business model for data preservation and publication: clients may preserve research results for up to 15 years and assign well-graded access rights, or to publish data with a DOI assignment for an unlimited period of time. Potential clients include libraries, research institutions, publishers and open platforms that desire an adaptable digital infrastructure to archive and publish data according to their institutional requirements and workflows.

Description: To address the need for published data, considerable effort has gone into formalizing the process of data publication. From funding agencies to publishers, data publication has rapidly become a requirement. Digital Object Identifiers (DOI) and data citations have enhanced the integration and availability of data. The challenge facing data publishers now is to deal with the increased number of publishable data products and most importantly the difficulties of publishing diverse data products into an online archive. The Oak Ridge National Laboratory Distributed Active Archive Center (ORNL DAAC), a NASA-funded data center, faces these challenges as it deals with data products created by individual investigators. This paper summarizes the challenges of curating data and provides a summary of a workflow solution that ORNL DAAC researcher and technical staffs have created to deal with publication of the diverse data products. The workflow solution presented here is generic and can be applied to data from any scientific domain and data located at any data center.

Description: The Archaeology Data Service (ADS) is a digital archive that has been promoting good practice in the use of digital archaeological data and supporting research, learning and teaching with high quality and dependable digital resources for twenty years. The ADS does this by preserving digital data in the long-term and by promoting and disseminating, open and free datasets, gathered from all sectors of archaeology. An integral component of the ADS remit has been the life-cycle principle of preservation, curation and dissemination of data in order to enable re-use. The ADS practices a combination of normalization, version migration, format migration and refreshment for the active management and ongoing preservation of all archived data types. This paper highlights the importance of the ongoing management of research data for long-term preservation. In particular this paper focuses on the challenges of migrating spatial data, specifically Computer Aided Design (CAD) files. Successful data migration of these files ensures that data is accessible and usable, and provides many opportunities through data re-use to combine and re-interrogate datasets, allowing new archaeological interpretations to be developed.

Description: Forest biomass is an important parameter for quantifying and understanding biological and physical processes on the Earth’s surface. Rapid, reliable, and objective estimations of forest biomass are essential to terrestrial ecosystem research. The Geoscience Laser Altimeter System (GLAS) produced substantial scientific data for detecting the vegetation structure at the footprint level. This study combined GLAS data with MODIS/BRDF (Bidirectional Reflectance Distribution Function) and ASTER GDEM data to estimate forest aboveground biomass (AGB) in Xishuangbanna, Yunnan Province, China. The GLAS waveform characteristic parameters were extracted using the wavelet method. The ASTER DEM was used to compute the terrain index for reducing the topographic influence on the GLAS canopy height estimation. A neural network method was applied to assimilate the MODIS BRDF data with the canopy heights for estimating continuous forest heights. Forest leaf area indices (LAIs) were derived from Landsat TM imagery. A series of biomass estimation models were developed and validated using regression analyses between field-estimated biomass, canopy height, and LAI. The GLAS-derived canopy heights in Xishuangbanna correlated well with the field-estimated AGB (R2 = 0.61, RMSE = 52.79 Mg/ha). Combining the GLAS estimated canopy heights and LAI yielded a stronger correlation with the field-estimated AGB (R2 = 0.73, RMSE = 38.20 Mg/ha), which indicates that the accuracy of the estimated biomass in complex terrains can be improved significantly by integrating GLAS and optical remote sensing data.

Description: It is generally acknowledged that the urban environment presents different types of risk factors, but how the structural effects of areas influence the risk levels in neighboring areas has been less widely investigated. This research assesses the local effects of burglary contributory factors on burglary over small areas in a large metropolitan region. A comparative framework is developed for analyzing the effects of geographic dependence on burglary rates, and for assessing how such dependence conditions the community context and the urban land use. A local indicators spatial autocorrelation analysis assesses burglaries over five years (2011–2015) to identify risk clusters. Thereafter, effects of different variables (e.g., unemployment, building density) on burglary frequency are estimated in a series of regression models while controlling for changes in the risk levels of nearby surrounding areas. Results uncover strong evidence that the configuration of the surroundings influences risk. After controlling for area-based interaction, patterns are identified that contrast with the previous literature, such as lower burglary frequency in areas with higher tenancy in social housing units. Together the findings demonstrate that the spatial arrangement of areas is as crucial as contextual crime factors, particularly when assessing the risk for small areas.

Description: In this paper we investigate the use of games technologies for the research and the development of 3D representations of real environments captured from GIS information and open source map data. Challenges involved in this area concern the large data-sets to be dealt with. Some existing map data include errors and are not complete, which makes the generation of realistic and accurate 3D environments problematic. The domain of application of our work is crisis management which requires very accurate GIS or map information. We believe the use of creating a 3D virtual environment using real map data whilst correcting and completing the missing data, improves the quality and performance of crisis management decision support system to provide a more natural and intuitive interface for crisis managers. Consequently, we present a case study into issues related to combining multiple large datasets to create an accurate representation of a novel, multi-layered, hybrid real-world maps. The hybrid map generation combines LiDAR, Ordnance Survey, and OpenStreetMap data to generate 3D cities spanning 1 km2. Evaluation of initial visualised scenes is presented. Initial tests consist of a 1 km2 landscape map containing up to 16 million vertices’ and run at an optimal 51.66 frames per-second.

Description: Tracking moving objects is both challenging and important for a large variety of applications. Different technologies based on the global positioning system (GPS) and video or radio data are used to obtain the trajectories of the observed objects. However, in some use cases, they fail to provide sufficiently accurate, complete and correct data at the same time. In this work we present an approach for fusing GPS- and video-based tracking in order to exploit their individual advantages. In this way we aim to combine the reliability of GPS tracking with the high geometric accuracy of camera detection. For the fusion of the movement data provided by the different devices we use a hidden Markov model (HMM) formulation and the Viterbi algorithm to extract the most probable trajectories. In three experiments, we show that our approach is able to deal with challenging situations like occlusions or objects which are temporarily outside the monitored area. The results show the desired increase in terms of accuracy, completeness and correctness.

Description: In this study, LIDAR DEM data was used to obtain a primary landform map in accordance with a well-known methodology. This primary landform map was generalized using the Focal Statistics tool (Majority), considering the minimum area condition in cartographic generalization in order to obtain landform maps at 1:1000 and 1:5000 scales. Both the primary and the generalized landform maps were verified visually with hillshaded DEM and an orthophoto. As a result, these maps provide satisfactory visuals of the landforms. In order to show the effect of generalization, the area of each landform in both the primary and the generalized maps was computed. Consequently, landform maps at large scales could be obtained with the proposed methodology, including generalization using LIDAR DEM.

Description: Due to the fact that geospatial information technology is considered necessary for disaster risk management (DRM), the need for more effective collaborations between providers and end users in data delivery is increasing. This paper reviews the following: (i) schemes of disaster risk management and collaborative data operation in DRM; (ii) geospatial information technology in terms of applications to the schemes reviewed; and (iii) ongoing practices of collaborative data delivery with the schemes reviewed. This paper concludes by discussing the future of collaborative data delivery and the progress of the technologies.

Description: With recent advances in sensor technologies, large amounts of movement data have become available in many application areas. A novel, promising application is the data-driven analysis of team sport. Specifically, soccer matches comprise rich, multivariate movement data at high temporal and geospatial resolution. Capturing and analyzing complex movement patterns and interdependencies between the players with respect to various characteristics is challenging. So far, soccer experts manually post-analyze game situations and depict certain patterns with respect to their experience. We propose a visual analysis system for interactive identification of soccer patterns and situations being of interest to the analyst. Our approach builds on a preliminary system, which is enhanced by semantic features defined together with a soccer domain expert. The system includes a range of useful visualizations to show the ranking of features over time and plots the change of game play situations, both helping the analyst to interpret complex game situations. A novel workflow includes improving the analysis process by a learning stage, taking into account user feedback. We evaluate our approach by analyzing real-world soccer matches, illustrate ISPRS Int. J. Geo-Inf. 2015, 4 2160 several use cases and collect additional expert feedback. The resulting findings are discussed with subject matter experts.

Description: The ever-increasing availability of linked open geospatial data provides an unprecedented source of geo-information to describe urban environments. This wealth of data should be turned into actionable knowledge: for example, open data could be used as a proxy or substitute for closed or expensive information. The successful employment of linked open geospatial data can pave the way for innovative solutions to smart city problems. In this paper, we illustrate a set of experiments that, starting from linked open geospatial data, execute a knowledge discovery process to predict urban semantics. More specifically, we leverage geo-information about points of interests as input in a classification model of land use at a moderate spatial resolution (250 meters) over wide urban areas in Europe. We replicate our experiments in different European cities—Milano, München, Barcelona and Brussels—to ensure the repeatability and generality of our approach, and we explain the experimental conditions, as well as the employed datasets to guarantee reproducibility. We extensively report on quantitative and qualitative evaluation results, to judge the validity, as well as the limitations of our proposed approach.

Description: We described in this report the essential geographic layers used as landscape drivers for the Marco Polo Argali habitat in the eastern Tajik Pamirs. Using remote sensing techniques and geographic information systems (GIS), individual layers were generated in order to acquire more information on argali patterns and habitat suitability and to make the dataset available online. We introduced an improved object-based image analysis in our mapping of the vegetation cover by utilizing spectral, topographic, and texture variables. We exhausted every Landsat image band and texture feature combination to select the best pairing of band-texture components. For vegetation class alone, the producer’s accuracy was 90.8% and the user’s accuracy was 91.6%.

Description: Unmanned Aerial Systems (UAS) are now capable of gathering high-resolution data, therefore, landslides can be explored in detail at larger scales. In this research, 132 aerial photographs were captured, and 85,456 features were detected and matched automatically using UAS photogrammetry. The root mean square (RMS) values of the image coordinates of the Ground Control Points (GPCs) varied from 0.521 to 2.293 pixels, whereas maximum RMS values of automatically matched features was calculated as 2.921 pixels. Using the 3D point cloud, which was acquired by aerial photogrammetry, the raster datasets of the aspect, slope, and maximally stable extremal regions (MSER) detecting visual uniformity, were defined as three variables, in order to reason fissure structures on the landslide surface. In this research, an Adaptive Neuro Fuzzy Inference System (ANFIS) and a Logistic Regression (LR) were implemented using training datasets to infer fissure data appropriately. The accuracy of the predictive models was evaluated by drawing receiver operating characteristic (ROC) curves and by calculating the area under the ROC curve (AUC). The experiments exposed that high-resolution imagery is an indispensable data source to model and validate landslide fissures appropriately.

Description: The target of the Open Geospatial Consortium (OGC) is interoperability of geographic information, which means creating opportunities to access geodata in a consistent, standardized way. In the domain of sensor data, the target will be picked up within the OGC Sensor Web Enablement Initiative and especially reached through the Sensor Observation Service (SOS) standard. This one defines a service for a standardized access to time series data and is usually used for in situ sensors (like discharge gauges and climate stations). Although the standard considers raster data, no implementation of the standard for raster data exists presently. In this paper an OGC-compliant Sensor Observation Service for a standardized access to raster data is described. A data model was developed that enables effective storage of the raster data with the corresponding metadata in a database, reading this data in an efficient way, and encoding it with result formats that the SOS-standard provides.

Description: In the aftermath of a devastating earthquake, public transportation is presumed paralyzed and thus unavailable; large numbers of people are expected to experience difficulty in commuting. In recent years, implementation of district continuity plans (DCPs) and business continuity plans (BCPs) has become a major concern for local governments and private firms, respectively. In this paper, we propose a pair of simulation models seeking to examine business commutation networks in terms of their possible obstruction and eventual restoration. The first of these model commuting intentions by analyzing individual daily commutes. The second offers a mobility model of commuters’ physical endurance for travel alternatives on foot or by bicycle. Next, we proceed to gauge the number of commuters likely to experience difficulty and adjudge their spatial distribution while taking into account such attributes as gender and employment. Lastly, we attempt to assess rates and patterns in the reduction of commutation constraints based on simulations that assume a restoration of rail infrastructure or its equivalent.

Description: Urban green space provides a series of esthetic, environmental and psychological benefits to urban residents. However, the relationship between the visibility of green vegetation and perceived safety is still in debate. This research investigated whether green vegetation could help to increase the perceived safety based on a crowdsourced dataset: the Place Pulse 1.0 dataset. Place Pulse 1.0 dataset, which was generated from a large number of votes by online participants, includes geo-tagged Google Street View images and the corresponding perceived safety score for each image. In this study, we conducted statistical analyses to analyze the relationship between perceived safety and green vegetation characteristics, which were extracted from Google Street View images. Results show that the visibility of green vegetation plays an important role in increasing perceived safety in urban areas. For different land use types, the relationship between vegetation structures and perceived safety varies. In residential, urban public/institutional, commercial and open land areas, the visibility of vegetation higher than 2.5 m has significant positive correlations with perceived safety, but there exists no significant correlation between perceived safety and the percentage of green vegetation in transportation and industrial areas. The visibility of vegetation below 2.5 m has no significant relationship with the perceived safety in almost all land use types, except for multifamily residential land and urban public/institutional land. In general, this study provided insight for the relationship between green vegetation characteristics and the perception of environment, as well as valuable reference data for developing urban greening programs.

Description: Cellular automaton (CA) is an important method in land use and cover change studies, however, the majority of research focuses on the discovery of macroscopic factors affecting LUCC, which results in ignoring the local effects within the neighborhoods. This paper introduces a Local Land Use Competition Cellular Automata (LLUC-CA) model, based on local land use competition, land suitability evaluation, demand analysis of the different land use types, and multi-target land use competition allocation algorithm to simulate land use change at a micro level. The model is applied to simulate land use changes at Jinshitan National Tourist Holiday Resort from 1988 to 2012. The results show that the simulation accuracies were 64.46%, 77.21%, 85.30% and 99.14% for the agricultural land, construction land, forestland and water, respectively. In addition, comparing the simulation results of the LLUC-CA and CA-Markov model with the real land use data, their overall spatial accuracies were found to be 88.74% and 86.82%, respectively. In conclusion, the results from this study indicated that the model was an acceptable method for the simulation of large-scale land use changes, and the approach used here is applicable to analyzing the land use change driven forces and assist in decision-making.

Description: Uncertainty in thematic maps has been tested mainly in maps with discrete or fuzzy classifications based on spectral data. However, many ecosystem maps in tropical countries consist of discrete polygons containing information on various ecosystem properties such as vegetation cover, soil, climate, geomorphology and biodiversity. The combination of these properties into one class leads to error. We propose a probability-based sampling design with two domains, multiple stages, and stratification with selection of primary sampling units (PSUs) proportional to the richness of strata present. Validation is undertaken through field visits and fine resolution remote sensing data. A pilot site in the center of the Colombian Andes was chosen to validate a government official ecosystem map. Twenty primary sampling units (PSUs) of 10 × 15 km were selected, and the final numbers of final sampling units (FSUs) were 76 for the terrestrial domain and 46 for the aquatic domain. Our results showed a confidence level of 95%, with the accuracy in the terrestrial domain varying between 51.8% and 64.3% and in the aquatic domain varying between 75% and 92%. Governments need to account for uncertainty since they rely on the quality of these maps to make decisions and guide policies.

Description: We study the double-cross matrix descriptions of polylines in the two-dimensional plane. The double-cross matrix is a qualitative description of polylines in which exact, quantitative information is given up in favour of directional information. First, we give an algebraic characterization of the double-cross matrix of a polyline and derive some properties of double-cross matrices from this characterisation. Next, we give a geometric characterization of double-cross similarity of two polylines, using the technique of local carrier orders of polylines. We also identify the transformations of the plane that leave the double-cross matrix of all polylines in the two-dimensional plane invariant.

Description: The interaction between human activity and landscape pattern has been a hot research topic during the last few decades. However, scholars used to measure human activity by social, economic and humanistic indexes. These indexes cannot directly reflect human activity and are not suitable for fine-grained analysis due to the coarse spatial resolution. In view of the above problems, this paper proposes a method that obtains the intensity of human activity from GPS trajectory data, collects landscape information from remote sensing images and further analyzes the interaction between human activity and landscape pattern at a fine-grained scale. The Lijiang River Basin is selected as the study area. Experimental results show that human activity and landscape pattern interact synergistically in this area. Built-up land and water boost human activity, while woodland restrains human activity. The effect of human activity on landscape pattern differs by the land cover category. Overall, human activities make natural land, such as woodland and water, scattered and fragmented, but cause man-built land, such as built-up land and farmland, clustered and regular. Nevertheless, human activities inside and outside urban areas are the opposite. The research findings in this paper are helpful for designing and implementing sustainable management plans.

Description: Automatic discovery of isolated land cover web map services (LCWMSs) can potentially help in sharing land cover data. Currently, various search engine-based and crawler-based approaches have been developed for finding services dispersed throughout the surface web. In fact, with the prevalence of geospatial web applications, a considerable number of LCWMSs are hidden in JavaScript code, which belongs to the deep web. However, discovering LCWMSs from JavaScript code remains an open challenge. This paper aims to solve this challenge by proposing a focused deep web crawler for finding more LCWMSs from deep web JavaScript code and the surface web. First, the names of a group of JavaScript links are abstracted as initial judgements. Through name matching, these judgements are utilized to judge whether or not the fetched webpages contain predefined JavaScript links that may prompt JavaScript code to invoke WMSs. Secondly, some JavaScript invocation functions and URL formats for WMS are summarized as JavaScript invocation rules from prior knowledge of how WMSs are employed and coded in JavaScript. These invocation rules are used to identify the JavaScript code for extracting candidate WMSs through rule matching. The above two operations are incorporated into a traditional focused crawling strategy situated between the tasks of fetching webpages and parsing webpages. Thirdly, LCWMSs are selected by matching services with a set of land cover keywords. Moreover, a search engine for LCWMSs is implemented that uses the focused deep web crawler to retrieve and integrate the LCWMSs it discovers. In the first experiment, eight online geospatial web applications serve as seed URLs (Uniform Resource Locators) and crawling scopes; the proposed crawler addresses only the JavaScript code in these eight applications. All 32 available WMSs hidden in JavaScript code were found using the proposed crawler, while not one WMS was discovered through the focused crawler-based approach. This result shows that the proposed crawler has the ability to discover WMSs hidden in JavaScript code. The second experiment uses 4842 seed URLs updated daily. The crawler found a total of 17,874 available WMSs, of which 11,901 were LCWMSs. Our approach discovered a greater number of services than those found using previous approaches. It indicates that the proposed crawler has a large advantage in discovering LCWMSs from the surface web and from JavaScript code. Furthermore, a simple case study demonstrates that the designed LCWMS search engine represents an important step towards realizing land cover information integration for global mapping and monitoring purposes.

Description: In this paper, the way topographic spatial information changes with resolution was investigated using semi-variograms and an Independent Structures Model (ISM) to identify the mechanisms involved in changes of topographic parameters as resolution becomes coarser or finer. A typical Loess Hilly area in the Loess Plateau of China was taken as the study area. DEMs with resolutions of 2.5 m and 25 m were derived from topographic maps with map scales of 1:10,000 using ANUDEM software. The ISM, in which the semi-variogram was modeled as the sum of component semi-variograms, was used to model the measured semi-variogram of the elevation surface. Components were modeled using an analytic ISM model and corresponding landscape components identified using Kriging and filter bank analyses. The change in the spatial components as resolution became coarser was investigated by modeling upscaling as a low pass linear filter and applying a general result to obtain an analytic model for the scaling process in terms of semi-variance. This investigation demonstrated how topographic structures could be effectively characterised over varying scales using the ISM model for the semi-variogram. The loss of information in the short range components with resolution is a major driver for the observed change in derived topographic parameters such as slope. This paper has helped to quantify how information is distributed among scale components and how it is lost in natural terrain surfaces as resolution becomes coarser. It is a basis for further applications in the field of geomorphometry.

Description: This article sets forth the early phases of a methodological proposal for designing and developing Volunteered Geographic Information (VGI) initiatives based on a system perspective analysis in which the components depend and interact dynamically among each other. First, it focuses on those characteristics of VGI projects that present different goals and modes of organization, while using a crowdsourcing strategy to manage participants and contributions. Next, a tool is developed in order to design the central crowdsourced processing unit that is best suited for a specific project definition, associating it with a trend towards crowd-based or community-driven approaches. The design is structured around the characterization of different ways of participating, and the task cognitive demand of working on geo-information management, spatial problem solving and ideation, or knowledge acquisition. Then, the crowdsourcing process design helps to identify what kind of participants are needed and outline subsequent engagement strategies. This is based on an analysis of differences among volunteers’ participatory behaviors and the associated set of factors motivating them to contribute, whether on a crowd or community-sourced basis. From a VGI system perspective, this paper presents a set of guidelines and methodological steps in order to align project goals, processes and volunteers and thus successfully attract participation. This methodology helps establish the initial requirements for a VGI system, and, in its current state, it mainly focuses on two components of the system: project and participants.

Description: This paper investigates the effect of four driving forces, including elevation, slope, distance to drainage and distance to major roads, on urban expansion in five Saudi Arabian cities: Riyadh, Jeddah, Makkah, Al-Taif and Eastern Area. The prediction of urban probabilities in the selected cities based on the four driving forces is generated using a logistic regression model for two time periods of urban change in 1985 and 2014. The validation of the model was tested using two approaches. The first approach was a quantitative analysis by using the Relative Operating Characteristic (ROC) method. The second approach was a qualitative analysis in which the probable urban growth maps based on urban changes in 1985 is used to test the performance of the model to predict the probable urban growth after 2014 by comparing the probable maps of 1985 and the actual urban growth of 2014. The results indicate that the prediction model of 2014 provides a reliable and consistent prediction based on the performance of 1985. The analysis of driving forces shows variable effects over time. Variables such as elevation, slope and road distance had significant effects on the selected cities. However, distance to major roads was the factor with the most impact to determine the urban form in all five cites in both 1985 and 2014.

Description: Increasing coastal space resource utilization (CSRU) activities and their impact on coastal environments has been recognized as a critical coastal zone stressor. Consequently, the need for sustainable and valid CSRU management has been highlighted. In this study, a highly-intelligent prototype decision-aided system for CSRU was developed. In contrast with existing coastal decision-aided systems, this system is aimed at the management of CSRU, providing reliable and dynamic numerical simulation, analysis, and aided decision making for real coastal engineering based on a self-developed fully automatic numerical program. It was established on multi-tier distributed architecture based on Java EE. The most efficient strategies for spatial data organization, automatic coastal numerical programs, and impact assessment modules are demonstrated. In addition, its integrated construction involving the addition of a new coastal project on the webpage, its one-click numerical prediction of coastal environmental impacts, assessments based on numerical results, and its aided decision-making capabilities are addressed. The system was applied to Ningbo Sea, China, establishing the Ningbo CSRU Decision Support System. Two projects were demonstrated: one reclamation project and one land-based outlet planning case. Results indicated that these projects had detrimental effects on local coastal environments. Therefore, the approvals of these projects were not recommended.