Visualizing spatiotemporal patterns of city service demand through a space‐time exploratory approach

Jing, Changfeng; Zhu, Yanli; Du, Mingyuan; Liu, Xintao

doi:10.1111/tgis.12820

Cited by 4 publications

(3 citation statements)

References 46 publications

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“…A recent attempt has been made to ensure high representativeness, and a data-driven approach was developed to identify the most suitable spatial unit for social media data (de Andrade et al, 2021). However, these approaches defined the unified size for the whole study area (Jing et al, 2021), which was often uncertain due to spatial heterogeneity. Therefore, what is the proper unit size and how to design it?…”

Section: Related Workmentioning

confidence: 99%

A hierarchical spatial unit partitioning approach for fine‐grained urban functional region identification

Jing

Zhang

et al. 2022

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Urban functional regions (UFRs) are formed and developed with human social actions and can reflect urban land use types. Appropriately identifying UFRs helps solve existing urban problems, optimize the spatial structure of cities, and provide a database for sustainable urban development. Most existing studies focus on developing novel methods and fusing multiple data sources, but neglect the impact of heterogeneous spatial units on UFR identification results. In this work, a hierarchical spatial unit partition approach was proposed to split the research area into many hierarchical units whilst considering the mixed degree of each unit. We further explored its capacity for identifying UFRs by integrating it with three widely used UFR identification methods. The results reveal that our proposed approach can correctly identify 10% more UFRs compared with the traditional grid‐based method, showing the efficiency of our proposed approach for identifying UFRs at a finer scale. This work provides support for those who are engaged in urban planning and urban policymaking, promoting urban sustainable development.

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Section: Related Workmentioning

confidence: 99%

A hierarchical spatial unit partitioning approach for fine‐grained urban functional region identification

Jing

Zhang

et al. 2022

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“…Urban open spaces, such as city squares, large shopping malls and stations, are characterized by dense crowds, frequent exchanges, and complex situations. As a result, these spaces often become primary targets for terrorism (Jing et al, 2021; Newburn, 2021; Qian et al, 2019), and the potential occurrence of emergencies, such as crowd stampedes (Zhang et al, 2017; Zhao et al, 2021), poses severe challenges to urban security. In urban open spaces, the perception and analysis of the temporal and spatial dynamics of crowds can provide key support for accurate decision‐making, efficient early warnings and emergency responses to relevant urban security issues (Draghici & Steen, 2018; Han, Li, Cui, Han, et al, 2019; Socha & Kogut, 2020).…”

Section: Introductionmentioning

confidence: 99%

Crowd sensing and spatiotemporal analysis in urban open space using multi‐viewpoint geotagged videos

Liu

Han

Song

et al. 2023

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Increasing concern for urban public safety has motivated the deployment of a large number of surveillance cameras in open spaces such as city squares, stations, and shopping malls. The efficient detection of crowd dynamics in urban open spaces using multi‐viewpoint surveillance videos continues to be a fundamental problem in the field of urban security. The use of existing methods for extracting features from video images has resulted in significant progress in single‐camera image space. However, surveillance videos are geotagged videos with location information, and few studies have fully exploited the spatial semantics of these videos. In this study, multi‐viewpoint videos in geographic space are used to fuse object trajectories for crowd sensing and spatiotemporal analysis. The YOLOv3‐DeepSORT model is used to detect a pedestrian and extract the corresponding image coordinates, combine spatial semantics (such as the positions of the pedestrian in the field of view of the camera) to build a projection transformation matrix and map the object recorded by a single camera to geographic space. Trajectories from multi‐viewpoint videos are fused based on the features of location, time, and directions to generate a complete pedestrian trajectory. Then, crowd spatial pattern analysis, density estimation, and motion trend analysis are performed. Experimental results demonstrate that the proposed method can be used to identify crowd dynamics and analyze the corresponding spatiotemporal pattern in an urban open space from a global perspective, providing a means of intelligent spatiotemporal analysis of geotagged videos.

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“…Recently, a number of studies have explored the spatial matching between service provision and the needs of user groups in order to achieve equalization of public services [19,20]. On the one hand, many studies have discussed the spatial layout of various public service facilities by using assessment methods such as accessibility assessment [21,22], population density assessment [4], and spatio-temporal visualization analysis assessment [23]. For example, some studies have explored the spatial matching relationship between urban green spaces and the population by using coupled coordination models [24].…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Public Service Facilities in 19 Large Cities in China from the Perspective of Supply and Demand

Wei

Ren

Guo

2022

Land

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The equalization of urban public service facilities is important to the daily lives of urban residents. Spatial quantification of the supply and demand of public service facilities can reveal relationships between supply and demand agents and provide a foundation for the planning of urban public service facilities. This study proposed a comprehensive framework to assess the current state of supply (accessibility of facilities) and demand (population carrying pressure) of various public services in cities and determine patterns between different public service facilities. This framework contains three elements: (a) multi-scale spatial quantification of the matching of supply and demand, (b) spatial matching of supply and demand, and (c) spatial clustering analysis of the supply and demand balance. This study analyzed 19 major cities in China from a supply and demand perspective and examined implications for matching the supply and demand of public service facilities. The results indicated that education service facilities had the most appropriate supply and demand relationship. Areas where public service facilities had a good matching of supply and demand demonstrated a strong pattern of clustering. There were significant differences in the level of matching of the supply and demand of public service facilities among various regions in China. The limitations of the framework and future directions are discussed.

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Visualizing spatiotemporal patterns of city service demand through a space‐time exploratory approach

Cited by 4 publications

References 46 publications

A hierarchical spatial unit partitioning approach for fine‐grained urban functional region identification

A hierarchical spatial unit partitioning approach for fine‐grained urban functional region identification

Crowd sensing and spatiotemporal analysis in urban open space using multi‐viewpoint geotagged videos

Evaluation of Public Service Facilities in 19 Large Cities in China from the Perspective of Supply and Demand

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