Research on spatial cluster detection of traffic crash (TC) at the city level plays an essential role in safety improvement and urban development. This study aimed to detect spatial cluster pattern and identify riskier road segments (RRSs) of TC constrained by network with a two-step integrated method, called NKDE-GLINCS combining density estimation and spatial autocorrelation. The first step is novel and involves in spreading TC count to a density surface using Network-constrained Kernel Density Estimation (NKDE). The second step is the process of calculating local indicators of spatial association (LISA) using Network-constrained Getis-Ord Gi* (GLINCS). GLINCS takes the smoothed TC density as input value to identify locations of road segments with high risk. This method was tested using the TC data in 2007 in Wuhan, China. The results demonstrated that the method was valid to delineate TC cluster and identify risk road segments. Besides, it was more effective compared with traditional GLINCS using TC counting as input. Moreover, the top 20 road segments with high-high TC density at the significance level of 0.1 were listed. These results can promote a better identification of RRS, which is valuable in the pursuit of improving transit safety and sustainability in urban road network. Further research should address spatial-temporal analysis and TC factors exploration.

With the coming era of big data and the rapid development and widespread applications of Geographical Information Systems (GISs), geocoding technology is playing an increasingly important role in bridging the gap between non-spatial data resources and spatial data in various fields. However, Chinese geocoding faces great challenges because of the complexity of the address string format in Chinese, which contains no delimiters between Chinese words, and the poor address management resulting from the existence of multiple address authorities spread among different governmental agencies. This paper presents a geocoding service based on an optimized Chinese address matching method, including address modeling, address standardization and address matching. The address model focuses on the spatial semantics of each address element, and the address standardization process is based on an address tree model. A geocoding service application is implemented in practice using a large quantity of data from Shenzhen Municipality. More than 1,460,000 data records were used to test the geocoding service, and good matching rates were achieved with good adaptability and intelligence.

Ischemic heart disease (IHD) is a leading cause of death worldwide. Urban public health and medical management in Shenzhen, an international city in the developing country of China, is challenged by an increasing burden of IHD. This study analyzed the spatio-temporal variation of IHD hospital admissions from 2003 to 2012 utilizing spatial statistics, spatial analysis, and space-time scan statistics. The spatial statistics and spatial analysis measured the incidence rate (hospital admissions per 1,000 residents) and the standardized rate (the observed cases standardized by the expected cases) of IHD at the district level to determine the spatio-temporal distribution and identify patterns of change. The space-time scan statistics was used to identify spatio-temporal clusters of IHD hospital admissions at the district level. The other objective of this study was to forecast the IHD hospital admissions over the next three years (2013–2015) to predict the IHD incidence rates and the varying burdens of IHD-related medical services among the districts in Shenzhen. The results show that the highest hospital admissions, incidence rates, and standardized rates of IHD are in Futian. From 2003 to 2012, the IHD hospital admissions exhibited similar mean centers and directional distributions, with a slight increase in admissions toward the north in accordance with the movement of the total population. The incidence rates of IHD exhibited a gradual increase from 2003 to 2012 for all districts in Shenzhen, which may be the result of the rapid development of the economy and the increasing traffic pollution. In addition, some neighboring areas exhibited similar temporal change patterns, which were also detected by the spatio-temporal cluster analysis. Futian and Dapeng would have the highest and the lowest hospital admissions, respectively, although these districts have the highest incidence rates among all of the districts from 2013 to 2015 based on the prediction using the GM (1,1). In addition, the combined analysis of the prediction of IHD hospital admissions and the general hospital distributions shows that Pingshan and Longgang might experience the most serious burden of IHD hospital services in the near future, although Futian would still have the greatest number and the highest incidence rate of hospital admissions for IHD.

The spatial distribution pattern of rural settlements in the Yellow River Basin is scattered and numerous. It is of great significance to study the discrete distribution of rural settlements for achieving high-quality development and promoting rural revitalization strategy. In this paper, we propose an enhanced evaluation model for assessing the spatial distribution dispersion of rural settlements, incorporating the weight of road grade (the road grade refers to the ranking of traffic capacity and importance of a particular type of road, indicating varying levels of time accessibility). We investigate the dispersion characteristics of rural settlements in the Yellow River Basin in 2020, focusing on both county and city scales. Furthermore, we conduct a comprehensive analysis of the spatial differentiation and scale effects of dispersion evaluation outcomes and their driving forces. Our findings reveal the following insights: (1) The road grade significantly influences the dispersion evaluation. When considering road grade in the dispersion calculation, the results align more closely with the actual situation. (2) The dispersion of rural settlements in the Yellow River Basin exhibits a decreasing trend from west to east. Specifically, the dispersion is higher in the upper reaches compared to the middle and lower reaches. Both city and county scales show spatial autocorrelation in dispersion, with a positive spatial correlation observed. High dispersion values cluster in the west, while low values concentrate in the east. Notably, the agglomeration degree is more pronounced at the county scale than at the city scale, highlighting more localized patterns of agglomeration and dispersion. (3) The multiscale geographically weighted regression model emerges as the optimal model for analyzing the driving forces of dispersion. At the city scale, factors such as river density, road density, and rural economy negatively impact dispersion. However, at the county scale, average elevation and rural economy positively affect dispersion, whereas river density, road density, and rural population density have a negative influence. By incorporating the weight of road grade into our evaluation model, we provide a more nuanced understanding of the spatial distribution dispersion of rural settlements in the Yellow River Basin. Our findings offer valuable insights for policymakers and planners seeking to optimize rural settlement patterns and promote sustainable rural development.

Geographic information systems (GISs) have shown considerable promise in enhancing textual semantic analysis. Current textual semantic analysis methods face significant limitations in accurately delineating semantic boundaries, identifying semantic clustering patterns, and representing knowledge evolution. To address these issues, this study proposes a framework that innovatively introduces GIS methods into textual semantic analysis and aligns them with the conceptual foundation of geographical metaphor theory. Specifically, word embedding models are employed to endow semantic primitives with comprehensive, high-dimensional semantic representations. GIS methods and geographical metaphors are subsequently utilized to project both semantic primitives and their relationships into a low-dimensional geospatial analog, thereby constructing a semantic space model that facilitates accurate delineation of semantic boundaries. On the basis of this model, spatial correlation measurements are adopted to reveal underlying semantic patterns, while knowledge evolution is represented using ArcGIS 10.7-based visualization techniques. Experiments on social media data validate the effectiveness of the framework in semantic boundary delineation and clustering pattern identification. Moreover, the framework supports dynamic three-dimensional visualization of topic evolution. Importantly, by employing specialized visualization methods, the proposed framework enables the intuitive representation of semantic symmetry and asymmetry within semantic spaces.

Long-term mining activities can cause complex ecological and environmental problems, including mineral surface exposure, dust-related disturbance, and vegetation degradation, while conventional ecological indices may not fully characterize the distinctive ecological conditions of mining areas. This study developed a mining-area Modified Remote Sensing Ecological Index (MRSEI) by incorporating the Lithological Mineral Index (LMI), Normalized Dust Difference Index (NDDI), and Vegetation Health Index (VHI) into the Remote Sensing Ecological Index (RSEI) framework. Object-based analytical units were constructed using Landsat time-series data from 2000 to 2024 and G-means clustering, followed by the development of an Object-Based XGBoost-SHAP (OB-XGBoost-SHAP) framework to quantify the relative model contributions and nonlinear associations of explanatory factors. The results showed that, in the Daye mining area (DMA), MRSEI provided clearer differentiation of ecological quality among land-use types than RSEI and Mining-Specific Eco-Environment Index (MSEEI), with an overall gradient of forest land > cultivated land > construction land > mining land. Annual Principal Component Analysis (Annual-PCA) and Global Principal Component Analysis (Global-PCA) results showed high consistency (Pearson r = 0.985). Across the seven evaluation years, the mean MRSEI of the DMA increased from 0.5493 in 2000 to 0.6035 in 2024, representing an overall change of 9.87%. The object-based model consistently outperformed the regular-grid model under random splitting, while model performance decreased under spatially separated validation but retained moderate predictive capability. Particulate Matter (PM10), Digital Elevation Model (DEM), Temperature (TEM), and Land Use Intensity (LU) showed relatively high model contributions, with their relative importance varying across different periods. This study integrates mining-specific ecological characterization, object-based spatial analysis, and explanatory-factor contribution analysis, providing a methodological reference for ecological quality assessment and long-term change monitoring in mining areas.