Search Results (5,393)

Modeling changes in ecosystem service value (ESV) resulting from land use/cover change (LUCC) in coastal regions play a crucial role in promoting regional sustainability and guiding policymaking. This study focuses on the Chaoshan region of China and analyzes the impact of land use changes in 2000, 2010, and 2020 on ESV. The Patch-generating Land Use Simulation (PLUS) model was used to simulate LUCC for 2030 under three different scenarios: natural development (ND), urban development (UD), and ecological protection (EP). The spatial distribution and aggregation degree of ESV were assessed to explore the intrinsic relationship between land use and ecosystem service value in the Chaoshan region. The results showed the following: (1) The cropland area in the Chaoshan region has significantly decreased, with the per capita cropland area dropping to 113.34 m² (0.028 acres) by 2020. The continuous expansion of construction land has been mainly concentrated in Shantou, Jieyang, and Chaozhou, with an increasingly evident trend of urban integration among these three cities. By 2030, the growth rate of construction land in the EP scenario is expected to decline, indicating a slowdown in urban expansion. (2) Between 2000 and 2020, Shantou was the only city in the region to experience a decline in total ESV. Low ESV values in the Chaoshan region are primarily concentrated in the southeastern area. As urban integration progresses, ESV values in this region are expected to continue to decline. (3) The ongoing trend of urban integration between Shantou, Chaozhou, and Jieyang may result in the region becoming an ecologically vulnerable area. Close monitoring of potential ecological risks in this area is crucial to ensure a balance between urban development and ecological protection. This study will provide important guidance for land use policies and sustainable development in the Chaoshan region, as well as in similar coastal cities globally. Full article

This study integrates customer loyalty program data with a synthetic population to analyze grocery shopping behaviours in Montreal. Using clustering algorithms, we classify 295,631 loyalty program members into seven distinct consumer segments based on behavioural and sociodemographic attributes. The findings reveal significant heterogeneity in consumer behaviour, emphasizing the impact of urban geography on shopping decisions. This segmentation also provides valuable insights for retailers optimizing store locations and marketing strategies and for policymakers aiming to enhance urban accessibility. Additionally, our approach strengthens agent-based model (ABM) simulations by incorporating demographic and behavioural diversity, leading to more realistic consumer representations. While integrating loyalty data with synthetic populations mitigates privacy concerns, challenges remain regarding data sparsity and demographic inconsistencies. Future research should explore multi-source data integration and advanced clustering methods. Overall, this study contributes to geographically explicit modelling, demonstrating the effectiveness of combining behavioural and synthetic demographic data in urban retail analysis. Full article

The cultural and creative industries have garnered widespread attention as an important vehicle for promoting the transformation and upgrading of urban industrial structures. In this investigation, we take Harbin—a city in China with a strong industrial legacy—as a case study. Through kernel density analysis and the DBSCAN clustering algorithm, we identify and analyze the spatial distribution and spatiotemporal evolution patterns of 157 clusters of cultural and creative industries in Harbin. We construct a Geographic Weighted Regression (GWR) model using 20 indicators from three categories (i.e., social, cultural, and economic) to analyze the factors impacting the agglomeration of cultural and creative industries in Harbin. Our findings reveal that the cultural and creative industries exhibit an agglomeration pattern. A large-scale agglomeration area for large enterprises has formed in the city center, while scattered, small-scale agglomeration zones for emerging small enterprises have formed in newly developed areas on the urban periphery. The GWR analysis indicates that economic factors have the most significant impact on the agglomeration of cultural and creative industries; however, night-time economic facilities show a negative correlation. Among social factors, the convenience of public transportation and new energy transportation infrastructure have a significant impact on industrial agglomeration. Regarding cultural factors, cultural and sports facilities have the greatest influence, while public information kiosks and public arts education facilities exhibit spatial variability. These findings provide a scientific basis for policy formulation and contribute to promoting the healthy development of cultural and creative industries. Full article

Spatial equity is a critical issue that the supply allocation should align with the level of demand, enabling all community members to equally benefit from the city’s resources and opportunities, yet commonly used assessment methods have inherent limitations. This study proposes a new framework to assess spatial equity based on the evaluation of similarity between GIS-based supply and demand maps and provides a simplified case study that assesses public transportation services across the area inside the Sixth Ring Road of Beijing to facilitate the comprehension of this framework. The results show that while services in this region are relatively spatially equitable, significant spatial inequity remains in certain areas, where targeted policy recommendations are put forward such as promoting innovative transportation solutions and redistributing excessive demand to less congested facilities. The application prospects and future development directions of the proposed framework are thoroughly discussed. This framework stands out for its ease of comprehension, visualization, and general applicability. Specifically, it is capable of identifying areas with severe inequity, thus contributing to the establishment of targeted intervention measures to mitigate spatial inequity. Full article

Advancements in large language models have significantly transformed higher education by integrating AI chatbots into course design, teaching, administration, and student support. This study evaluates the use, effectiveness, and perceptions of chatbots in a Python-based graduate-level GIS programming course at a U.S. university. Students self-reported perceived improvements in skills and the use of different help resources across three home assignments of varying complexity and spatial context. In group discussions, students shared their experiences, strategies, and envisioned future applications of chatbots in GIS programming and beyond. The results indicate that prior programming experience enhances students’ perception of assignment usefulness, and that chatbots serve as a partial replacement for traditional help resources (e.g., websites) in completing assignments. Overall, students expressed positive sentiments regarding chatbot effectiveness, especially for complex spatial tasks. While students were optimistic about the potential of chatbots to enhance future learning, concerns were raised about overreliance on AI, which could hinder the development of independent problem-solving and programming skills. In conclusion, this study offers valuable insights into optimizing chatbot integration in GIS education. Full article

Densification is a widely used concept, but there is a lack of terminology and tools to facilitate discussions among data scientists, policy makers and citizens. This paper proposes a model of building changes based on building surveys undertaken in past decades to connect discussions about densification with shared evidence. A specific challenge is to process buildings in city regions and areas in a replicable way across different building data sources. Another challenge is to manage the quality of the representation, i.e., how well the maps represent changes to buildings and how well they can support discussions of densification. Building data and real buildings are different things that sometimes change in an independent way. Addressing these factors requires different forms of expertise, i.e., expertise about the realities depicted in the areas studied, about local data sources, and about advanced matching tools and state-of-the-art densification concepts. We present a collaborative dashboard through which to engage corresponding experts in the production of building change maps and the clarification of related concepts. Full article

Recognition and detection of building groups are core tasks in cartographic research. Current recognition methods that rely on spatial and geometric features often neglect semantic aspects, failing to account for the complex relationships between buildings and their real-world semantic associations. This limitation hampers the ability to fully capture human understanding of the real world. Based on this, this paper proposes a novel method for building group recognition that integrates both spatial geometric and semantic features. The method effectively identifies building group structures by considering spatial proximity, geometry, and semantic similarity. First, spatial proximity between buildings is defined by constructing a neighborhood graph based on Delaunay triangulation, and the spatial geometric features of each building are extracted. The spatial distance and semantic intensity relationships between Point of Interest (POI) data and buildings are used for semantic feature extraction. Subsequently, a spatial–semantic dual clustering strategy is applied in two stages to aggregate the buildings and generate preliminary grouping results. Finally, the grouping results are refined through an optimal graph segmentation strategy, which ensures both global and local optimization. The proposed method is applied to two areas in Shenzhen City, China, and the experimental results demonstrate that, compared with other methods, it more effectively identifies building groups with coherent spatial, geometric, and semantic features, improving the Adjusted Rand Index (ARI) from 0.589 to 0.701. This approach provides significant support for intelligent map generalization and personalized knowledge services in the era of big data. Full article

In recent years, Digital Participatory Platforms (DPPs) have become an increasingly popular tool for citizen participation in community planning processes. They serve municipalities, citizen initiatives, and other planning authorities as digital tools to collect feedback, discuss ideas, solve problems and monitor small-scale planning processes within their communities. In addition, DPPs facilitate the integration of the spatial domain into participatory community planning. In this paper, we assess the most important Critical Success Factors (CSFs) of participatory community planning with geospatial DPPs, and analyze the potential, opportunities, and challenges associated with integrating these platforms into community planning. We analyze the results of a digital questionnaire that we shared with a selected group of expert scholars and community stakeholders. We then contextualize this feedback with our experiences from the piloting phase and commercial roll-out of the ‘Bürgercockpit’-application for participatory community planning within the Austrian Agenda21-framework. As a result, we identify the most important CSFs of participatory community planning with geospatial DPPs. This set of CSFs should provide a better orientation on how to complement well-established analog participatory methods and practices with geospatial DPPs for the co-production of shared visions and solutions, ultimately empowering all stakeholders of a planning process to better manage their communities. Full article

An Earth system spatial grid (ESSG) is an extension of a discrete global grid system (DGGS) in the radial direction. It is an important tool for organizing, representing, simulating, analyzing, sharing, and visualizing spatial data. The existing ESSGs suffer from complex spatial relationships and significant geometric distortion. To mitigate these problems, a spherical geodesic degenerate octree grid (SGDOG) and its encoding and decoding schemes are proposed in this paper. The SGDOG extends the great circle arc QTM in the radial direction and adopts different levels of the great circle arc QTM at different radial depths. The subdivision of SGDOG is simple and clear, and has multi-level characteristics. The experimental results demonstrate that the SGDOG has advantages of simple spatial relationships, convergent volume distortion, and real-time encoding and decoding. The SGDOG has the potential to organize and manage global spatial data and perform large-scale visual analysis of the Earth system. Full article

In recent years, there has been a sharp increase in the number of online fraud cases. However, research on crime geography has paid little attention to online crimes, especially to the influencing factors behind their spatial distributions. Online fraud is closely related to people’s daily internet use. The existing literature has explored the impact of internet use on online crimes based on small samples of individual interviews. There is a lack of large-scale studies from a community perspective. This study applies the routine activity theory to online activities to test the relationship between online fraud alert data and the usage durations of different types of mobile phone users’ applications (apps) for communities in ZG City. It builds negative binomial regression models for analyzing the impact of the usage of different types of apps on the spatial distribution of online fraud. The results reveal that the online fraud crime rate and the online time spent on a financial management app share the most similar spatial distribution. While financial management, online education, transportation, and search engine app usages have a significant positive association with online fraud, the use of a financial management app has the greatest impact. Additionally, time spent on social media, online shopping and entertainment, and mobile reading apps have a significant negative association with online fraud. As not all online activities lead to cybercrime, crime prevention efforts should target specific types of apps, such as financial management, online education, transportation, and search engines. Full article

Topological relations form the backbone of qualitative spatial reasoning and, as such, play a paramount role in geographic information systems. Three decades of research have provided a proliferation of sets of qualitative topological relations in both continuous and discretized spaces, but only in continuous spaces has the concept of organizing these relations into a larger framework (called a conceptual neighborhood graph) been considered. Previous work leveraged matrix differences to derive the anisotropic scaling neighborhood for these relations. In this paper, a simulation protocol is used to derive conceptual neighborhood graphs of qualitative topological relations in ${\mathbb{Z}}^2$ for the operations of translation and isotropic scaling. It is further shown that, when aggregating raster relations into their continuous counterparts and collapsing neighborhood connections within these groups, the familiar conceptual neighborhood structures for continuous regions appear. Full article

The catastrophic twin earthquakes that struck southern Turkey and northern Syria on 6 February 2023 caused massive casualties and extensive damage to infrastructure, with Hatay Province of Turkey bearing the brunt of the impact. To swiftly and thoroughly assess the damage caused by the earthquakes and the subsequent reconstruction efforts, this study initially investigated the application of light change ratios between the pre-earthquake monthly nighttime lights (NTLs) and the post-earthquake daily NTL data to identify earthquake damage in Hatay Province. Next, the monthly NTL data were employed to calculate the time series average lighting index (ALI). Subsequently, random noise and seasonal fluctuation were eliminated through data smoothing and seasonal decomposition techniques. Pre- and post-earthquake regression models were then utilised to establish a comprehensive framework for assessing economic recovery following the earthquake. The findings indicated that (1) the seismic damage identification method based on NTL data achieved an overall accuracy exceeding 71.55% in detecting building damage after a disaster. This method provided a swift and effective solution for rapidly assessing disaster-related destruction. (2) The reduced NTLs exhibited a strong correlation with the area of severely and moderately damaged buildings while showing a weaker correlation with areas of slightly damaged buildings. (3) The developed pre- and post-earthquake regression models demonstrated a high degree of fit, making them valuable tools for assessing regional economic recovery after the earthquake. At the county scale, such districts as Erzin and Kumlu exhibited promising signs of recovery, while such severely impacted areas as Antakya faced misconceptions of progress, primarily due to the brightening of NTLs caused by reconstruction efforts. Additionally, such districts as Dortyol and Samandag grappled with substantial short-term recovery challenges. Although the province experienced gradual economic recovery, achieving complete restoration has remained complex and time-intensive. The study offers valuable insights into earthquake damage assessment and economic recovery monitoring while serving as a scientific reference for disaster mitigation and post-disaster reconstruction efforts. Full article

Population aging is a pressing global issue. As it progresses, older adults’ demand for public transport will increase. Ensuring their equitable access is vital for social equity. Meanwhile, physiological changes and travel preferences in older adults create unique bus usage patterns, making them more susceptible to the built environment. To test this, we compared bus travel behavior between older adults and young people in Wuhan, China. Our results showed that older adults travel more often, with a longer morning peak and less pronounced evening peak. We developed the GWRBoost model, combining Geographic Weighted Regression (GWR) and eXtreme Gradient Boosting (XGBoost), to explore the spatial heterogeneity and nonlinear impact of the built environment on bus travel for both groups. The study found significant differences in how the built environment affects bus ridership between older adults and young people. For older adults, proximity to the nearest bus stop is most critical, regardless of weekday or weekend. These variables also show spatial variations and nonlinear relations with bus ridership for both groups. These findings improve our understanding of older adults’ travel and offer insights for optimizing their travel environment and promoting transportation equity. Full article

With the development of autonomous driving technology, the application scenarios for mobile robots and unmanned vehicles are gradually expanding from simple structured environments to complex unstructured scenes. In unstructured three-dimensional spaces such as urban environments, traditional two-dimensional map construction and path planning techniques struggle to effectively plan accurate paths. To address this, this paper proposes a method of constructing a map and planning a route based on three-dimensional spatial representation. This method utilizes point cloud semantic segmentation to extract navigable space information from environmental point cloud data and employs voxelization techniques to generate a voxel map. Building on this, the paper combines a variable search neighborhood A* algorithm with a road-edge-detection-based path adjustment to generate optimal paths between two points on the map, ensuring that the paths are both short and capable of effectively avoiding obstacles. Our experimental results in multi-story parking garages show that the proposed method effectively avoids narrow areas that are difficult for vehicles to pass through, increasing the average edge distance of the path by 83.3% and significantly enhancing path safety and feasibility. Full article

(1) Background: This study investigates the impact of the COVID-19 pandemic on tourists’ perceptions of community-based ecotourism (CBET) in Romania and Spain, taking into account country of origin and economic development. In order to provide insights for sustainable tourism development and policymaking, this study aims to investigate how the COVID-19 pandemic has influenced tourists’ perceptions of community-based ecotourism (CBET) in Romania and Spain, taking into account differences in country of origin and economic development. (2) Methods: An online questionnaire was administered to 703 ecotourists (353 Romanian, 350 Spanish). (3) Results: The results show statistically significant differences between the two countries regarding the perception of ecotourism principles, information sources and preferred activities. For example, Romanians showed stronger agreement with ecotourism’s positive contribution to local communities and minimal environmental impact than Spaniards (p < 0.01 for EP3, EP4 and EP6). Significant correlations were found between specific ecotourism elements and preferred activities within each country, highlighting different preferences. Multilinear regression analysis showed that gender and region of origin significantly predicted perceptions of the role of ecotourism in biodiversity conservation for Spain. (4) Conclusions: Policy recommendations include targeted awareness campaigns, increased community involvement and cross-cultural collaboration to promote sustainable CBET development. This comparative study fills a gap in CBET research by contrasting perceptions in Eastern and Western European countries with different levels of economic development. Full article