Evaluation of Community Livability Using Gridded Basic Urban Geographical Data—A Case Study of Wuhan

Abstract

The evaluation of community livability quantifies the demands of human settlement at the micro scale, supporting urban governance decision-making at the macro scale. Big data generated by the urban management of government agencies can provide an accurate, real-time, and rich data set for livability evaluation. However, these data are intertwined by overlapping geographical management boundaries of different government agencies. It causes the difficulty of data integration and utilization when evaluating community livability. To address this problem, this paper proposes a scheme of partitioning basic geographical space into grids by optimally integrating various geographical management boundaries relevant to enterprise-level big data. Furthermore, the system of indexes on community livability is created, and the evaluation model of community livability is constructed. Taking Wuhan as an example, the effectiveness of the model is verified. After the evaluation, the experimental results show that the livability evaluation with reference to our basic geographic grids can effectively make use of governmental big data to spatially identify the multi-dimensional characteristics of a community, including management, environment, facility services, safety, and health. Our technical solution to evaluate community livability using gridded basic urban geographical data is of large potential in producing thematic data of community, constructing a 15-min community living circle of Wuhan, and enhancing the ability of the community to resist risks.

CRITIC-TOPSIS Based Evaluation of Smart Community Governance: A Case Study in China

“…For example, technical systems such as the smart community integrated multi-generation energy system [27], smart community information security encryption scheme [28], and smart community governance system [26] are designed to assist smart community governance. Meanwhile, empirical progress has been made in the evaluation of community livability through building the evaluation indicator system [29]. An evaluation index system for transportation in smart communities has been designed to deal with the transportation problems that often occur in the smart community [30].…”

Research agenda for using artificial intelligence in health governance: interpretive scoping review and framework

“…Further, AI capabilities such as spatial and temporal-based analyses [ 10 ] can be used to achieve worldwide pillar goals [ 22 ], and indicators at macro and micro levels [ 16 ], to support decision-making at both national and global scales [ 37 ]. Structural topic modeling (STM) can be applied to a text to reveal topic network and topic trends to perform analyses that are based on frequency, proportion, and importance over time and space (conceptual, temporal, and geographical trends [ 38 ]. Participation of different actors based on geographical units that affect health inputs or outputs, such as achieving goals, disease burden, and health costs, provide a new situation for the system [ 16 ].…”

“…Indices and indicators such as happiness [ 53 ], quality of life [ 54 ], community livability [ 38 ], equity [ 22 ], and democratic decisions through citizen participation [ 55 ] have been discussed in previous AI studies. AI can be used to improve goals, objectives, or indicators at both national [ 31 ] and international plans.…”

“…AI is applicable to solve complex environmental and social life problems by integrating different inputs and Indicators, e.g., the government effectiveness index, economic policy, the share of the health sector from GDP, the rule of law, health expenditure per capita, control of corruption index, Human Development Index, and out-of-pocket expenditures [ 16 , 23 , 38 , 77 ].…”

Assessing urban livability in Shanghai through an open source data-driven approach

“…This can reduce the reliance of the assessment on official statistics (which might be unavailable, of inconsistent quality or even manipulated) and enable the use of open-source, readily available and updated granular data at the same time. However, despite the growing popularity of such open data-driven approaches to studying urban phenomena (Hou & Zheng, 2021;Luo et al, 2022;Ye et al, 2020), there are substantial gaps in the literature about their application to urban livability assessments. For example, although big data facilitated analysis validity, a large amount of datasets also led to decision paralysis or made it hard to quickly find out useful information, and thus generalization of big data was needed to conduct livability evaluation (Wey & Huang, 2018).…”