ALBERT

Description: A spatial and temporal heterogeneity analysis of residential land prices, in general, is crucial for maintaining high-quality economic development. Previous studies have attempted to explain the geographical evolution rule by studying spatial-temporal heterogeneity, but they have neglected the contextual information, such as school district, industrial zone, population density, and job density, associated with residential land prices. Therefore, in this study, we consider contextual factors and propose a revised local regression algorithm called the contextualized geographically and temporally weighted regression (CGTWR), to effectively address spatiotemporal heterogeneity, and to creatively extend the feasibility of importing the contextualization into the GTWR model. The quantitative impact of contextual information on residential land prices was identified in Shijiazhuang (SJZ) city from 1974 to 2021. Empirical analyses demonstrated that school district and industrial zone factors played important roles in residential land prices. Notably, the distance from a residential area to an industrial zone was significantly positively correlated with residential land prices. In addition, a positive relationship between school districts and residential land prices was also observed. Finally, the R2 value of the CGTWR model was 92%, which was superior to those of ordinary least squares (OLS, 76%), geographically weighted regression (GWR, 85%), contextualized geographically weighted regression (CGWR, 86%), and GTWR (90%) models. These evaluation results indicate that the CGTWR algorithm, which incorporates contextual information and spatiotemporal variation, could provide policy makers with evidence for understanding the nature of varying relationships within a land price dataset in China.

Description: Land-use classification is fundamental for environmental and water resource evaluation in coastal plain areas. However, comprehensive remote sensing image-based land-use analysis is challenged by the lack of massive remote sensing images and the massive computing power of large-scale server systems. In this paper, the spatial-temporal land-use change characteristics of the Hangzhou Bay area coastal plain are investigated on the Google Earth Engine platform. The proposed model uses a random forest algorithm to assist the land-use classification. The dataset is selected from the year 2009 to 2020 and classified with an average classification accuracy of 89% and Kappa coefficient of 88%. The results show that the land use in the selected region is affected by urbanization, the balance of cultivated land occupation and compensation, construction of economic development zone, and other activities. The investigation also shows that in the past 12 years, land use has changed rapidly, and each land-use type maintains the dynamic balance of occupation and compensation. Although the overall land-use distribution is stable, the information entropy fluctuates at a high level, with an average value of 1.15, and the multi-year average value of equilibrium is as high as 0.83. The driving force of land-use change is analyzed and accounted as demographics and human population dynamics, social-economic development, urbanization, and coupling effects of the above-mentioned factors.

Description: Protected areas (PA) are effective means for protecting biodiversity, but less is known about their effect on the social-ecological system (SES). Using a semi-experimental approach and a descriptive case study based evaluation, we analyzed the effect of a PA in the Yucatan Peninsula on land-cover and household resource management strategies in time and space (before and after the PA establishment; inside and outside its limits). To assess the changes of land-use practices in the areas surrounding the communities inside and outside the PA, and their change over time (from 2003 to 2015), we used remote sensing analysis and semi-structured interviews. Our results show that after the PA was established, the forest increased and agricultural plots decreased inside and to a lesser extent outside the PA. However, fires reduced the area of old-growth forest and increased young secondary forest, highlighting the system’s vulnerability to uncommon events. Resource management strategies were also affected: while inside the PA households tended toward specializing on tourism, outside the PA household strategies implied a diversification of productive activities. Overall, the establishment of the PA proved to be an effective tool to promote forest recovery and prevent deforestation in the regions surrounding the communities both inside and outside the PA.

Description: Terraced tourism sustainability depends on maintenance for terraced landscape. The effective measures for protecting terraces rest on the cooperation among multi-stakeholders. Therefore, a multi-stakeholder involvement mechanism (MSIM) is very important for terraced landscape conservation. Dazhai village in Longji Terraces, Guangxi Province, Southwest China, explored a MSIM to maintain terraced tourism sustainability in the past 20 years. Based on the statistical data and the data from household interviews, this study analyses the development history of Dazhai tourism. Comparing the changes in different stages in components of MSIM of Dazhai, we revealed the successful key factors of MSIM for maintaining terraces tourism sustainability includes identifying accurately core attractions and stakeholders, an effective communication strategy, a democratic decision-making mechanism, dynamic benefit distribution schemes (BDS) facing terraced conservation, coordination teams with foresight and selflessness, and the effective supervision and management. In the end, we conclude that economic income is the most important driver stimulating villagers in Dazhai to insist on growing rice. It is crucial factor to drive farmers to protect their terraces. but economic income improvement of the households depends on a systemic and dynamic MSIM. The most important causes for Dazhai MSIM success results from the wise coordination teams and the effective communication strategy.

Description: Climate change is expected to lead to changes to the amount, frequency, intensity, and timing of precipitation and subsequent water supply and its availability to plants in mountain regions worldwide. This is likely to affect plant growth and physiological performance, with subsequent effects to the functioning of many important high-elevation ecosystems. We conducted a quantitative systematic review and meta-analysis of the effects of altered water supply on plants from high elevation ecosystems. We found a clear negative response of plants to decreases in water supply (mean Hedges’ g = −0.75, 95% confidence intervals: −1.09 to −0.41), and a neutral response to increases in water supply (mean Hedges’ g = 0.10, 95% confidence intervals: 0.43 to 0.62). Responses to decreases in water supply appear to be related to the magnitude of change in water supply, plant growth form, and to the measured response attribute. Changes to precipitation and water supply are likely to have important consequences for plant growth in high elevation ecosystems, with vegetation change more likely be triggered by reductions than increases in growing season precipitation. High elevation ecosystems that experience future reductions in growing-season precipitation are likely to exhibit plant responses such as reduced growth and higher allocation of carbohydrates to roots.

Description: Urban spatial structure reflects the organization of urban land use and is closely related to the travel patterns of residents. The characteristics of urban spatial structure include both static and dynamic aspects. The static characteristics of urban spatial structure reflect the morphological features of space, and the dynamic characteristics of urban spatial structure reflect intra-city functional linkages. With the continuous agglomeration of population and industries; megacities have become the core spatial carriers leading China’s social and economic development; and their urban spatial structure has also been reconstructed. However; there is still a certain lack of understanding of the characteristics of the spatial structure of China’s megacities. This study aimed to reveal characteristics of the spatial structure of Chinese megacities at different scales using jobs-housing big data. To achieve this goal, spatial autocorrelation and a geographically weighted regression (GWR) model were applied to reveal static polycentricity, and community detection was used to reveal dynamic commuting communities. The distribution of jobs in urban space and jobs–housing balance levels in commuting communities were further analyzed. Experiments were conducted in Tianjin, China. We found that: (1) the static characteristics of the spatial structure of megacities presented the coexistence of polycentricity and a high degree of dispersion at macro- and meso-scales; (2) the dynamic characteristics of the spatial structure of megacities revealed two types of commuting communities at macro- and meso-scales and most commuting communities had a good jobs-housing balance. These findings can be referenced by urban managers and planners to formulate relevant policies for spatial distribution optimization of urban functions and transportation development at different spatial scales.

Description: In recent decades, China has been on a new journey toward a digital economy of which e-commerce accounts for a substantial proportion. Despite some controversy, the innovation diffusion hypothesis and efficiency hypothesis of online shopping have been tested in research on the urban–rural dual structure. However, research on the spatial diffusion model of online business is sparse. Based on the online business and online shopping index released by the Ali Research Institute, this article compares the spatial diffusion model of online shopping and online business in the core–periphery structure based on the inequality between the eastern and western regions of China. Our study suggests that online business trends are in line only with the innovation diffusion hypothesis, with marginal counties having lower levels of online business. Online shopping, on the other hand, is in line with the innovation diffusion hypothesis and partially with the efficiency hypothesis, with a higher index of online shopping in the core regions and some peripheral counties. The discrepancy in the spatial diffusion mode is due to the differences in aims and supporting elements between online business and online shopping. Apart from infrastructure, the diffusion of online business is largely constrained by the regional industrial base, while online shopping is influenced by income and savings levels, which is the main reason for the differences in the spatial diffusion of online business and online shopping. We argue that the diffusion of online business has not led to the ability to balance regional inequalities at the national scale, while online shopping has the potential to bridge core and peripheral disparities better than online businesses, not in terms of the ability to bridge economic disparities, but in terms of the potential to reduce spatial consumption inequalities and welfare gains.

Description: In 2021, the Biden administration signed an executive order to protect 30% of American lands by 2030. Accomplishing this ambitious goal in the U.S. requires understanding the relative contribution of public and private lands toward supporting biodiversity. New approaches are needed because existing approaches focus on quantity of habitat without incorporating quality. To fill this need, we developed a 30 m resolution national habitat condition index (HCI) that integrates quality and quantity measures of habitat. We hypothesized that including an evaluation of the quality of habitat at landscape scales, both in conservation-focused preserves and working lands would provide a better assessment of the value of geographies for conservation. We divided the conterminous U.S. by major land cover type and into natural and cultivated lands and then spatially mapped multiple anthropogenic stressors, proximity to aquatic habitat, and vegetation departure from expected natural disturbance regimes. Each map layer was then scored for site impact and distance decay and combined into a final national index. Field observations providing scored relative ecological conditions were used for HCI calibration and validation at both CONUS and regional scales. Finally, we evaluate lands by management (conservation versus working lands) and ownership (public versus private) testing the value of these lands for conservation. While we found regional differences across CONUS, functional habitat was largely independent of protection status: working lands provide clear habitat and other values. These results are relevant for guiding strategies to achieve the U.S. 30 by 30 goals. Where similar data exist in other countries, analogous modeling could be used to meet their national conservation commitments.

Description: In this study, the presence and management of the social networks of 78 tourist destinations were analyzed for the development of sustainable tourism, with particular attention being paid to Spanish smart destinations. The variables for the empirical analysis were determined from a literature review, and it was concluded from a descriptive analysis, correlation analysis and an analysis of variance, that although the presence of tourist destinations on the internet and on social networks was notable, their management was inadequate for the development of sustainable tourist destinations. It was shown that there is a direct relationship between the number of visitors at a tourist destination and its presence on social networks. However, our analyses found that this correlation was not related to social network management; a greater number of visitors were not related to the effective management of social networks. It was concluded that smart destinations, despite having a technological advantage, did not stand out for their presence and management on social networks. The manuscript ends with some recommendations for the future

Description: This paper explores the issues of contemporary spatial differentiation in the process of industry concentration and the accompanying specialization according to the intensity of research and development in urban agglomerations, as exemplified by Wrocław and its suburbs. The aim of the study was to assess the degree and directions of advancement of these processes and to identify spatial patterns in the location and relocation of industry operators in the analyzed area. The industrial location was analyzed on the basis of industry operators registered in section C (industrial processing) according to the Polish Classification of Activities (PKA) in city districts in 2008 and 2016. The industry’s spatial structures were presented using the classification of industrial processing according to R&D intensity broken down into high-, medium-high-, medium-low-, and low-tech industry sectors. Quantitative methods were used to analyze and identify the degree of spatial concentration and specialization of the industry. The underlying analytical statistics included relative indicators, such as the location quotient (LQ), Florence’s location quotient, or the Krugman Specialization Index, as well as analyses of market structures (Herfindahl–Hirschman index or discrete index). The findings of this research revealed quite significant changes taking place in the internal spatial structure of Wrocław and its immediate vicinity in terms of the industry concentration and specialization. This phenomenon was much more dynamic and turbulent in the post-socialist countries than in the cities of Western Europe. Likewise, Wrocław and its suburban zone are characterized by a significant diversification and intensity of the processes subject to this analysis. These processes proved to be complex and selective.