Assessing Community-Level Livability Using Combined Remote Sensing and Internet-Based Big Geospatial Data

Abstract

With rapid urbanization, retrieving livability information of human settlements in time is essential for urban planning and governance. However, livability assessments are often limited by data availability and data update cycle, and this problem is more serious when making an assessment at finer spatial scales (e.g., community level). Here we aim to develop a reliable and dynamic model for community-level livability assessment taking Linyi city in Shandong Province, China as a case study. First, we constructed a hierarchical index system for livability assessment, and derived data for each index and community from remotely sensed data or Internet-based geospatial data. Next, we calculated the livability scores for all communities and assessed their uncertainties using Monte Carlo simulations. The results showed that the mean livability score of all communities was 59. The old urban and newly developed districts of our study area had the best livability, and got a livability score of 62 and 58 respectively, while industrial districts had the poorest conditions with an average livability score of 48. Results by dimension showed that the old urban district had better conditions of living amenity and travel convenience, but poorer conditions of environmental health and comfort. The newly developed districts were the opposite. We conclude that our model is effective and extendible for rapidly assessing community-level livability, which provides detailed and useful information of human settlements for sustainable urban planning and governance.

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

“…where l i is the objective weight, α is the adjustment ratio of objective weight and subjective weight, and (1 − α) is the proportion of subjective weight. Normally, the subjective and objective weights are equal, and α equals 0.5 by default [36]. According to the experimental data statistical results in the previous literature [30], livability is positively correlated with urban housing prices.…”

Big Geospatial Data or Geospatial Big Data? A Systematic Narrative Review on the Use of Spatial Data Infrastructures for Big Geospatial Sensing Data in Public Health

“…Geography (general, remote sensing, geoscience) [12][13][14][15][16][17][18][19][20][21] 10 Public health [22][23][24][25][26][27][28][29][30][31] 10 Environment (physical, built environment) [32][33][34][35][36][37][38][39] 8 Science (computer, engineering, multidisciplinary) [40][41][42][43] 4…”

“…China [13][14][15][23][24][25][26][27][28][29][30][33][34][35][36][37] 16 India [41] 1 Indonesia [31] 1…”

Latent factor analysis and measurement on sustainable urban livability in Siliguri Municipal Corporation, West Bengal through EFA and CFA model

“…Urban unit-based livability assessment also plays a crucial role for inter-regional comparison of livability. The index of livability assessment mainly constructed on the basis of either on composite index, Analytical Hierarchical Process (AHP) or on Remote sensing based (Mushtaha et al, 2020;Satu & Chiu, 2017;Valcárcel-Aguiar et al, 2018;Zhu et al, 2020). Zhu et al, 2020, constructed a reliable and dynamic model for livability assessment in Lyni city of Shanghai provinces in China based on Remote Sensing and Internet oriented geospatial data.…”

“…The index of livability assessment mainly constructed on the basis of either on composite index, Analytical Hierarchical Process (AHP) or on Remote sensing based (Mushtaha et al, 2020;Satu & Chiu, 2017;Valcárcel-Aguiar et al, 2018;Zhu et al, 2020). Zhu et al, 2020, constructed a reliable and dynamic model for livability assessment in Lyni city of Shanghai provinces in China based on Remote Sensing and Internet oriented geospatial data. Shabanzadeh Namini et al ( 2019) evaluated the livability indicators of the metropolitan Tehran districts of Iran.…”