Natural Cities Generated from All Building Locations in America
Authorities define cities—or human settlements in general—through imposing top-down rules in terms of whether buildings belong to cities. Emerging geospatial big data makes it possible to define cities from the bottom up, i.e., buildings determine themselves whether they belong t...
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| Format: | Article |
| Language: | English |
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MDPI AG
2019-04-01
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| Series: | Data |
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| Online Access: | https://www.mdpi.com/2306-5729/4/2/59 |
| _version_ | 1828350008495177728 |
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| author | Bin Jiang |
| author_facet | Bin Jiang |
| author_sort | Bin Jiang |
| collection | DOAJ |
| description | Authorities define cities—or human settlements in general—through imposing top-down rules in terms of whether buildings belong to cities. Emerging geospatial big data makes it possible to define cities from the bottom up, i.e., buildings determine themselves whether they belong to a city using the notion of natural cities and based on head/tail breaks, which is a classification and visualization tool for data with a heavy-tailed distribution. In this paper, we used 125 million building locations—all building footprints of America (mainland) or their centroids more precisely—to generate 2.1 million natural cities in the country (see the URL as shown in the note of Figure 1). In contrast to government defined city boundaries, these natural cities constitute a valuable data source for city-related research. |
| first_indexed | 2024-04-14T01:18:20Z |
| format | Article |
| id | doaj.art-e6b4b8eb20dd4009bd7db667e0a885f3 |
| institution | Directory Open Access Journal |
| issn | 2306-5729 |
| language | English |
| last_indexed | 2024-04-14T01:18:20Z |
| publishDate | 2019-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Data |
| spelling | doaj.art-e6b4b8eb20dd4009bd7db667e0a885f32022-12-22T02:20:46ZengMDPI AGData2306-57292019-04-01425910.3390/data4020059data4020059Natural Cities Generated from All Building Locations in AmericaBin Jiang0Faculty of Engineering and Sustainable Development, Division of GIScience, University of Gävle, SE-801 76 Gävle, SwedenAuthorities define cities—or human settlements in general—through imposing top-down rules in terms of whether buildings belong to cities. Emerging geospatial big data makes it possible to define cities from the bottom up, i.e., buildings determine themselves whether they belong to a city using the notion of natural cities and based on head/tail breaks, which is a classification and visualization tool for data with a heavy-tailed distribution. In this paper, we used 125 million building locations—all building footprints of America (mainland) or their centroids more precisely—to generate 2.1 million natural cities in the country (see the URL as shown in the note of Figure 1). In contrast to government defined city boundaries, these natural cities constitute a valuable data source for city-related research.https://www.mdpi.com/2306-5729/4/2/59head/tail breaksnatural citiesZipf’s lawgeospatial big data |
| spellingShingle | Bin Jiang Natural Cities Generated from All Building Locations in America Data head/tail breaks natural cities Zipf’s law geospatial big data |
| title | Natural Cities Generated from All Building Locations in America |
| title_full | Natural Cities Generated from All Building Locations in America |
| title_fullStr | Natural Cities Generated from All Building Locations in America |
| title_full_unstemmed | Natural Cities Generated from All Building Locations in America |
| title_short | Natural Cities Generated from All Building Locations in America |
| title_sort | natural cities generated from all building locations in america |
| topic | head/tail breaks natural cities Zipf’s law geospatial big data |
| url | https://www.mdpi.com/2306-5729/4/2/59 |
| work_keys_str_mv | AT binjiang naturalcitiesgeneratedfromallbuildinglocationsinamerica |