Big Data-Driven Measurement of the Service Capacity of Public Toilet Facilities in China
Public health facility planning is one of the important contents of national land planning, which needs to balance geospatial equity and service capacity. However, assessment models and data acquisition methods based on a geosystemic analysis perspective have been lacking for a long time. By focusin...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-05-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/12/9/4659 |
| _version_ | 1797505546135273472 |
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| author | Bo Fu Xiao Xiao Jingzhong Li |
| author_facet | Bo Fu Xiao Xiao Jingzhong Li |
| author_sort | Bo Fu |
| collection | DOAJ |
| description | Public health facility planning is one of the important contents of national land planning, which needs to balance geospatial equity and service capacity. However, assessment models and data acquisition methods based on a geosystemic analysis perspective have been lacking for a long time. By focusing on urban public toilets and taking the highly urbanized city of Shenyang, China as the study area, this study developed a new data strategy for urban public facilities with points of interests (POI) big data as the main data source, and subsequently corrected the POI data and analyzed the errors through a field survey, and conducted an empirical assessment oriented toward spatial equity and service capacity to discover the development dynamics of urban facilities over the past ten years and the impacting factors. We found that the integrated population and spatial elements could more accurately evaluate the service capacity of public toilets. Meanwhile, POI data have value in the research of public health facilities, but there are some errors in data quality and data access. The study empirically explores the geographic analysis methods of field research data (small data) and POI data (big data) with empirical contributions. |
| first_indexed | 2024-03-10T04:20:08Z |
| format | Article |
| id | doaj.art-904111ffa13c4a0da09e47ecaa3fac90 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-10T04:20:08Z |
| publishDate | 2022-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-904111ffa13c4a0da09e47ecaa3fac902023-11-23T07:52:09ZengMDPI AGApplied Sciences2076-34172022-05-01129465910.3390/app12094659Big Data-Driven Measurement of the Service Capacity of Public Toilet Facilities in ChinaBo Fu0Xiao Xiao1Jingzhong Li2Institute of Applied Ecology, Chinese Academy of Sciences, Shenyang 110016, ChinaInstitute of Applied Ecology, Chinese Academy of Sciences, Shenyang 110016, ChinaKey Lab for Environmental Computation and Sustainability of Liaoning Province, Shenyang 110016, ChinaPublic health facility planning is one of the important contents of national land planning, which needs to balance geospatial equity and service capacity. However, assessment models and data acquisition methods based on a geosystemic analysis perspective have been lacking for a long time. By focusing on urban public toilets and taking the highly urbanized city of Shenyang, China as the study area, this study developed a new data strategy for urban public facilities with points of interests (POI) big data as the main data source, and subsequently corrected the POI data and analyzed the errors through a field survey, and conducted an empirical assessment oriented toward spatial equity and service capacity to discover the development dynamics of urban facilities over the past ten years and the impacting factors. We found that the integrated population and spatial elements could more accurately evaluate the service capacity of public toilets. Meanwhile, POI data have value in the research of public health facilities, but there are some errors in data quality and data access. The study empirically explores the geographic analysis methods of field research data (small data) and POI data (big data) with empirical contributions.https://www.mdpi.com/2076-3417/12/9/4659urban sanitation infrastructurespatial justiceservice capacitypublic toiletsChina |
| spellingShingle | Bo Fu Xiao Xiao Jingzhong Li Big Data-Driven Measurement of the Service Capacity of Public Toilet Facilities in China Applied Sciences urban sanitation infrastructure spatial justice service capacity public toilets China |
| title | Big Data-Driven Measurement of the Service Capacity of Public Toilet Facilities in China |
| title_full | Big Data-Driven Measurement of the Service Capacity of Public Toilet Facilities in China |
| title_fullStr | Big Data-Driven Measurement of the Service Capacity of Public Toilet Facilities in China |
| title_full_unstemmed | Big Data-Driven Measurement of the Service Capacity of Public Toilet Facilities in China |
| title_short | Big Data-Driven Measurement of the Service Capacity of Public Toilet Facilities in China |
| title_sort | big data driven measurement of the service capacity of public toilet facilities in china |
| topic | urban sanitation infrastructure spatial justice service capacity public toilets China |
| url | https://www.mdpi.com/2076-3417/12/9/4659 |
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