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Urban spatial elements present agglomeration and dispersion geographic processes in the urban development. Identifying the characteristics of their distribution changes and accurately capturing the evolution of the urban spatial structure is of great significance to urban construction and management. This study takes Wuhan as a case study and focuses on the spatial agglomeration distribution of urban elements. Point of Interest (POI) data from 2017 to 2021 were collected, and the Block2Vec model was employed to extract the comprehensive geographic information from various elements within the traffic analysis zones (TAZs). Subsequently, identification and division were carried out to access the level of urban spatial element agglomeration. Finally, the spatial–temporal evolution characteristics of urban aggregated elements in the Wuhan metropolitan development area over five years were compared and analyzed. The results indicate the following: (1) urban elements present an obvious circle structure in their spatial agglomeration, with distinct differences observed among different element types; (2) from 2017 to 2021, the Wuhan urban development zone experienced obvious expansion in urban space; (3) increased agglomeration of spatial elements mainly occurred in the surrounding areas of the city, while some areas in the city center displayed weaker element agglomeration and a reduction in various service facilities. The results demonstrate that the method used in this study could effectively identify the spatial agglomeration distribution of urban elements, as well as accurately distinguishing regions with distinct development characteristics. This approach could provide robust support for optimizing land use and urban spatial planning. [ABSTRACT FROM AUTHOR]

Global aging is getting worse, especially in China, a country with a large population. It is urgently needed to plan the site of new urban elderly care facilities for an aging society. Based on point of interest data and machine learning algorithms, we established a site selection model of urban elderly care facilities for Wuhan in China and selected potential optimal sites for new urban elderly care facilities. We found that 2059 of the 31,390 grids with a resolution of 500 m × 500 m of Wuhan are priority layout grids for new urban elderly care facilities. A total of 635 priority grids were further selected based on the agglomeration degree of the aging population in each street. They are mainly distributed in the areas with a concentrated aging population within the Second Ring Road around the urban centers. Additionally, some outer suburban streets with a relatively high aging degree also require immediate facility construction. The point of interest data and machine learning algorithms to select the location of urban elderly care facilities can optimize their overall configuration and avoid the subjectivity of site selection to some degree, provide empirical support for how to achieve a good configuration of "population–facilities" in space, and continuously improve the science of the spatial allocation of elderly care facilities. [ABSTRACT FROM AUTHOR]

The extent of urban built⁃up areas is important to understand the current status and development of cities, also is one of the preconditions to implement the control of urban boundaries and manage the layout of urban functional space. In the past, the accuracy of urban built⁃up areas extraction using nighttime light imageries is often limited by data resolution and spatial scales. The Luojia ⁃1A satellite, launched on 2018⁃06⁃02, provides a new source of nighttime light data at 130⁃meter resolution, which has great potential in extent extraction of urban built⁃up areas. In this paper, integrating Luojia ⁃1A nighttime light data with Landsat 8 multispectral imageries and point of interest (POI), the extent of built⁃up areas in Wuhan, China has been extracted using human settlement index (HSI) and thresholds, respectively. The results of Luojia ⁃1A data have been compared with results obtained from visible infrared imager radiometer suite （VIIRS） data using corresponding methods. The results show that the extracted urban extent extracted by HSI using Luojia⁃1A data possesses the highest accuracy, with a Kappa coefficient of 0.769. Correspondingly, the Kappa coefficient of built-up areas extent obtained by HSI method using VIIRS data is 0.702. The accuracies of urban extent extracted by integrating Luojia ⁃1A and VIIRS images with POIs are relatively lower, which have respectively present Kappa coefficients of 0.618 and 0.574. The Kappa coefficients of extraction accuracies using simple thresholds segementation(STS) method form raw Luojia⁃1A and VIIRS images are 0.503 and 0.482. The results reveal that Luojia⁃1A nighttime light data benefit urban extent extraction for its finer resolution and more abundant spatial details, compared with its predecessors such as DMSP/OLS and VIIRS. [ABSTRACT FROM AUTHOR]

Anthropogenic activities associated with various new technologies are increasingly disrupting the geochemical cycles of rare earth elements (REEs). For example, samarium (Sm) and gadolinium (Gd) have emerged as microcontaminants in the natural waters of developed areas. Surface water samples of 13 urban lakes were collected in Wuhan, the largest city in central China, with a population of over 11 million. The aim of this study was to examine to what extent REE anomalies occur and the relationship between the concentration of anthropogenic REEs in lakes and the surrounding environment. In this study, based on land-use type and point of interest (POIs) data, buffer extraction, density estimation and Spearman correlation analysis were first proposed to identify different sources of anthropogenic REEs, which mainly included hospitals, factories, population, urban land and cropland. The PAAS-normalized REE patterns indicate that all lake samples display pronounced positive Sm and Gd anomalies, ranging from 5.92 to 19.88 and 1.73 to 14.97, respectively. Spearman correlation analysis showed that hospital density was positively correlated with anthropogenic Gd concentration, and a positive relationship between proportion of cropland and the concentration of anthropogenic Sm. By utilizing Gd anth , Sm anth , and the conventional ion ratio (NO 3 −/Cl−), a three-dimensional tracer system was established, and the system accurately obtained a characterization of the impact of WWTPs, hospitals, factories and agriculture on the lakes. Moreover, the results from this hydrochemical method were consistent with the analysis of geographic information systems, which indicated that this anthropogenic contaminant as a tracer was reliable for analysing the source of urban water pollution. [Display omitted] • REEs were studied in urban lakes of the largest city in central China. • Agriculture, hospitals and WWTP greatly affected the patterns of REEs in urban water environment. • The highest anomaly Gd value was found in the central region, decreasing with distance from urban core. • First reported that anthropogenic Sm concentration linearly correlates to the proportion of cropland around the lakes. • Combination of anthropogenic REEs and traditional ions could provide promising insights into tracing human activities. [ABSTRACT FROM AUTHOR]

Some studies on the impact of traditional land use factors on traffic crashes do not take into account the limitations of spatial heterogeneity and spatial scale. To overcome these limitations this study presents a systematic method based on multi-scale geographically weighted regression (MGWR), which considers spatial heterogeneity and spatial scale differences of different influencing factors, to explore the influence of reclassified points-of-interest (POI) on traffic crashes occurring on weekdays and weekends. Experiments were conducted on 442 communities in Hankou, Wuhan, and the performance of the proposed method was compared against traditional methods based on ordinary least squares (OLS), spatial lag model (SLM), spatial error model (SEM), and geographically weighted regression (GWR). The experiments show that the proposed method yielded the best fitness of models and more accurate model results of local coefficient estimates. The highlights of the results are as follows: There are differences in the scale of the predictor variables. Residential POI, scenic POI, and transportation POI have a global effect on traffic crashes. Commercial service POI and industrial POI affects traffic crashes at the regional scale, while public service POI affects crashes at the local scale. The local coefficient estimates from residential POI and scenic POI have little impact on traffic crashes. During weekdays, more transportation POI in the entire study area leads to more traffic crashes. While on weekends, transportation POI has a significant positive effect on crashes only in some communities. The local coefficient estimates for industrial POI vary at different periods. Commercial service POI and public service POI may increase the risk of crashes in some communities, which can be observed on weekdays and weekends. Exploring the influence of POI on traffic crashes at different periods is helpful for traffic management strategies and in reducing traffic crashes. [ABSTRACT FROM AUTHOR]

Rapid urban expansion has brought new challenges to firefighting, with the speed of firefighting rescue being crucial for the safety of property and life. Thus, fire prevention and rescuing people in distress have become more challenging for city managers and emergency responders. Unfortunately, existing research does not consider the negative effects of the current spatial distribution of fire-risk areas, land cover, location, and traffic congestion. To address these shortcomings, we use multiple methods (including geographic information system, multi-criterion decision-making, and location–allocation (L-A)) and multi-source geospatial data (including land cover, point-of-interest, drive time, and statistical yearbooks) to identify suitable areas for fire brigades. We propose a method for identifying potential fire-risk areas and to select suitable fire brigade zones. In this method, we first remove exclusion criteria to identify spatially undeveloped zones and use kernel density methods to evaluate the various fire-risk zones. Next, we use analytic hierarchy processes (AHPs) to comprehensively evaluate the undeveloped areas according to the location, orography, and potential fire-risk zones. In addition, based on the multi-time traffic situation, the average traffic speed during rush hour of each road is calculated, a traffic network model is established, and the travel time is calculated. Finally, the L-A model and network analysis are used to map the spatial coverage of the fire brigades, which is optimized by combining various objectives, such as the coverage rate of high-fire-risk zones, the coverage rate of building construction, and the maintenance of a sub-five-minute drive time between the proposed fire brigade and the demand point. The result shows that the top 50% of fire-risk zones in the central part of Wuhan are mainly concentrated to the west of the Yangtze River. Good overall rescue coverage is obtained with existing fire brigades, but the fire brigades in the north, south, southwest, and eastern areas of the study area lack rescue capabilities. The optimized results show that, to cover the high-fire-risk zones and building constructions, nine fire brigades should be added to increase the service coverage rate from 93.28% to 99.01%. The proposed method combines the viewpoint of big data, which provides new ideas and technical methods for the fire brigade site-selection model. [ABSTRACT FROM AUTHOR]