Multiperspective Evaluation Model for the Spatial Distribution of Public Service Facilities Based on Service Capability and Subjective Preferences

Zhan, Yujie; Sui, Lintong; Wang, Meng; Huang, Jiehua; Zhu, Jinwei; Chen, Di; Fan, Jiemeng

doi:10.1061/(asce)up.1943-5444.0000834

Cited by 7 publications

(4 citation statements)

References 37 publications

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“…The fusion of these datasets allows for the integration of social media and NTL data, thereby yielding more accurate and comprehensive results in the identification of urban agglomeration spaces [52]. By leveraging these diverse types of data, we can more effectively identify the spatial range of urban agglomerations, circumventing some of the difficulties and errors inherent in traditional methods [53]. In summary, the method of fusing social media data enables the acquisition of more comprehensive and precise results in identifying urban agglomeration spaces.…”

Section: Discussionmentioning

confidence: 99%

Identifying the Spatial Range of the Pearl River Delta Urban Agglomeration by Fusing Nighttime Light Data with Weibo Sign-In Data

Cao,

Liu,

Yang

2024

IJGI

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Accurately identifying the spatial range of urban agglomerations holds significant practical importance for the precise allocation of various elements and coordinated development within urban agglomerations. However, current research predominantly focuses on the physical spaces of urban agglomerations, overlooking their sphere of influence. This study begins with the spatial interactions of population elements within urban agglomerations and fuses Weibo sign-in data with NTL data to identify the spatial range of urban agglomerations. It further compares and validates the results before and after the fusion of data. The results reveal that the accuracy of identifying the spatial range of urban agglomerations with the fusion of NTL data and Weibo sign-in data has improved by 7%, with a Kappa increase of 0.1766 compared to using NTL data alone, which indicates that fusing social media data can significantly enhance the accuracy of identifying the spatial range of urban agglomerations. This study proposes a novel approach for identifying the spatial range of urban agglomerations through the fusion of NTL data and social media data from a data fusion perspective. On one hand, it supplements the application of data fusion in the study of urban agglomeration spaces; on the other hand, it accurately identifies the spatial range of urban agglomerations, which holds great practical value for the sustainable development of urban agglomerations.

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Section: Discussionmentioning

confidence: 99%

Identifying the Spatial Range of the Pearl River Delta Urban Agglomeration by Fusing Nighttime Light Data with Weibo Sign-In Data

Cao,

Liu,

Yang

2024

IJGI

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“…Community elderly care stations (CECSs) are set to provide six types of services, including daycare, health guidance, cultural entertainment, and so on [47]. CECSs usually have rooms to support service delivery, such as a dining room, infirmary, activity room, etc., to meet the common needs of the elderly for aging in place [48,49], and the area of CECSs generally do not exceed 1500 m 2 . However, based on field surveys and related studies, the utilization rates of CECSs in Dongcheng and Xicheng Districts are generally very low, and most of the CECSs are used by a few people.…”

Section: Study Areamentioning

confidence: 99%

Factors Influencing the Usage Frequency of Community Elderly Care Facilities and Their Functional Spaces: A Multilevel Based Study

Wen,

Zhang,

et al. 2024

Buildings

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The construction of community elderly care facilities (CECF) is pivotal for promoting healthy aging and “aging in place” for older people. This study focuses on the low utilization rates of community elderly care facilities in the Dongcheng and Xicheng Districts, core areas of Beijing. The explainable machine learning method is used to analyze data across three dimensions: the elderly’s individual attributes, characteristics of the community elderly care station (CECS), and features of the built environment around CECS and subdistrict, to identify the important factors that influence the usage frequency of overall CECS and its different functional spaces, and also the correlation between factors and usage frequency of CECS. It shows that the most important factors are the features of CSCF, including the degree of space acceptance and satisfaction with services provided, which influence the usage frequency of nine functional spaces (R2 ≥ 0.68) and overall (R2 = 0.56). In addition, older people’s individual factors, such as age and physical condition, significantly influence the usage of specific spaces such as rehabilitation therapy rooms and assistive bathing rooms. The influence of built environment characteristics is relatively low, with factors such as the density of bus stations and housing prices within the subdistrict and the mean distance from CECF to the nearest subway stations being more important. These findings provide a reference for the construction of indoor environments, management of service quality, and optimal site selection for future community elderly care facilities.

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“…At the macro-scale, studies have delved into the rationality of urban spatial layout, with a focus on aspects such as the job-housing balance and dynamics, emphasizing the geographical and functional alignment between living and working spaces [25,26]. At the micro-scale, research has focused on the utilization of community public facilities, residents' satisfaction [27], accessibility [28], and the contact level with different facilities [29,30].…”

Section: Multi-source Data For Measuring Living Conveniencementioning

confidence: 99%

Enhancing Urban Living Convenience through Plot Patterns: A Quantitative Morphological Study

Chen,

Guo,

Liu

et al. 2024

Buildings

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Living convenience in public service facilities has attracted significant attention as a crucial indicator of urban development and quality improvement. However, the performance of plot patterns—a fundamental unit for precise control when measuring urban quality—influencing living convenience requires in-depth exploration. This study integrates multiple urban datasets with quantitative urban morphology methods to investigate the impact of various plot pattern features on living convenience. Specifically, we investigate the Inner Ring area of Shanghai as an empirical case. The assessment considers the diverse effects of facilities at different distances, accounting for the life radiuses of both older individuals and the general population. Additionally, the analysis of plot patterns includes planar and three-dimensional aspects, controlling key variables such as road network accessibility and centrality. The results indicate that, for small-scale plots, shape has a strong impact, while, for large-scale plots, the division and construction intensity within a block has a stronger influence. Furthermore, plots of different area types have different recommended construction intensities. Notably, for large-scale blocks, it is advisable to maintain a building density of around 0.3. In short, this study contributes to human-centered planning by providing targeted recommendations to address the existing deficiencies in plot morphology regulation and control from the perspective of quantitative urban morphology.

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Multiperspective Evaluation Model for the Spatial Distribution of Public Service Facilities Based on Service Capability and Subjective Preferences

Cited by 7 publications

References 37 publications

Identifying the Spatial Range of the Pearl River Delta Urban Agglomeration by Fusing Nighttime Light Data with Weibo Sign-In Data

Identifying the Spatial Range of the Pearl River Delta Urban Agglomeration by Fusing Nighttime Light Data with Weibo Sign-In Data

Factors Influencing the Usage Frequency of Community Elderly Care Facilities and Their Functional Spaces: A Multilevel Based Study

Enhancing Urban Living Convenience through Plot Patterns: A Quantitative Morphological Study

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