Community scale livability evaluation integrating remote sensing, surface observation and geospatial big data

Zhang, Yuan; Li, Qiangzi; Wang, Hongyan; Du, Xin; Huang, Huaguo

doi:10.1016/j.jag.2019.04.018

Cited by 31 publications

(17 citation statements)

References 47 publications

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“…Pollution generated by the concentration of transportation and industry in urban areas has been of increasing concern when urbanization outpaces societal capacity to implement pollution control measures [3]. In particular, air pollution is one of the most important factors that influences human health and livability, especially in developing countries [16]. Our study area is famous for wood industry, but is also plagued by air pollutants due to the production of wood products.…”

Section: Constructing An Assessment Index Systemmentioning

confidence: 99%

“…In contrast, at the middle level, livability assessments are made for spatial units within the city such as blocks, communities, and artificially delineated grids. It considers both local property information and environmental background on broader scales, which are able to truly reflect living environment of urban inhabitants [16][17][18]. Therefore, middle-level assessment (e.g., community-level) can provide detailed livability information that sufficiently supports urban planning and governance.…”

Section: Introductionmentioning

confidence: 99%

“…These methods are effective to capture the perceptions and satisfaction of urban dwellers to their living environment, which is useful for understanding the evolution mechanisms of urban structure and their driving forces, and provides insights into human-oriented urban planning [23]. However, making interview and questionnaire surveys is time-and labor-intensive, rendering it difficult to update data and extensively conduct planning in multiple cities [16]. Moreover, the survey cannot cover all communities, and thus cannot meet the requirements of urban governance which often needs livability information as exhaustive as possible [13].…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2020

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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.

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Section: Constructing An Assessment Index Systemmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

et al. 2020

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“…In this study, a web crawler was developed to acquire Easygo heat map data from May 6th to May 11th, 2019, at 20:00, 21:00 and 22:00 during after-work hours on workdays. The population density of these time periods was averaged to represent the permanent population distribution [35].…”

Section: Geospatial Big Datamentioning

confidence: 99%

“…In the context of dasymetric mapping, the accuracy of the population estimates depends greatly on the covariates used [27]. Remote sensing products and geospatial big data are the most commonly used covariates in dasymetric mapping [1,3,18,[28][29][30][31][32][33][34][35]. Remotely sensed population-related products, such as land use/land cover (LULC) data and nighttime light (NTL) data, could show the actual surface conditions that reflect the physical factors that affect the population distribution.…”

mentioning

confidence: 99%

Improving the Accuracy of Fine-Grained Population Mapping Using Population-Sensitive POIs

Zhao

Zhang

et al. 2019

Remote Sensing

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Many methods have been used to generate gridded population maps by downscaling demographic data. As one of these methods, the accuracy of the dasymetric model depends heavily on the covariates. Point-of-interest (POI) data, as important covariates, have been widely used for population estimation. However, POIs are often used indiscriminately in existing studies. A few studies further used selected categories of POIs identified based only on the nonspatial quantitative relationship between the POIs and population. In this paper, the spatial association between the POIs and population distribution was considered to identify the POIs with a strong spatial correlation with the population distribution, i.e., population-sensitive POIs. The ability of population-sensitive POIs to improve the fine-grained population mapping accuracy was explored by comparing the results of random forest dasymetric models driven by population-sensitive POIs, all POIs, and no POIs, along with the same sets of multisource remote sensing and social sensing data. The results showed that the model driven by population-sensitive POI had the highest accuracy. Population-sensitive POIs were also more effective in improving the population mapping accuracy than were POIs selected based only on their quantitative relationship with the population. The model built using population-sensitive POIs also performed better than the two popular gridded population datasets WorldPop and LandScan. The model we proposed in this study can be used to generate accurate spatial population distribution information and contributes to achieving more reliable analyses of population-related social problems.Dasymetric mapping, on the other hand, was used to generate the 1-km LandScan [5] and the 100-m WorldPop [18] annual population datasets.Dasymetric mapping is a well-known cartographic approach for generating high-resolution population maps [22][23][24]. The idea of dasymetric mapping is to generate a weighting layer using different kinds of covariates. This layer will then be used to redistribute the demographic data. Dasymetric mapping has been demonstrated to be more effective in generating more accurate population estimates than other approaches [25,26]. In the context of dasymetric mapping, the accuracy of the population estimates depends greatly on the covariates used [27]. Remote sensing products and geospatial big data are the most commonly used covariates in dasymetric mapping [1,3,18,[28][29][30][31][32][33][34][35]. Remotely sensed population-related products, such as land use/land cover (LULC) data and nighttime light (NTL) data, could show the actual surface conditions that reflect the physical factors that affect the population distribution. However, these data often lack semantic information. Semantic information can be used to describe the relationship between geographic attributes and geographic features, such as different human activities occurring at accurate geographic locations. With the development of location-aware devices and the emergence and p...

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Are school-district houses truly livable against the background of educational capitalization? evidence from the city of Hohhot in western China

Zou,

Rao,

Guo

et al. 2023

J Hous and the Built Environ

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Community scale livability evaluation integrating remote sensing, surface observation and geospatial big data

Cited by 31 publications

References 47 publications

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

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

Improving the Accuracy of Fine-Grained Population Mapping Using Population-Sensitive POIs

Are school-district houses truly livable against the background of educational capitalization? evidence from the city of Hohhot in western China

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