Identifying Urban Built-Up Areas Based on Spatial Coupling between Nighttime Light Data and POI: A Case Study of Changchun
Abstract
:1. Introduction
2. Literature Review
2.1. Application of Nighttime Light Data in Research
2.2. Study of Nighttime Light Data Combined with POI
2.3. Summary
3. Methodology
3.1. Study Area Overview and Data Sources
3.1.1. Study Area Overview
3.1.2. Data Resource
3.1.3. Data Preprocessing
3.2. Extraction of Built-Up Areas
- (i)
- The data underwent pre-processing and nighttime lighting data for March 2022 were extracted using ArcGIS.
- (ii)
- The potential threshold value was set and the area of built-up area under this threshold value was calculated as follows: Let the maximum gray value be and the minimum gray value be 0. is the nighttime light data image of Changchun City in March. Let the area of the built-up area of Changchun based on statistics for the same period be . Then, the potential threshold of the nighttime light data image of Changchun City is calculated as:The area of built-up area extracted under the threshold is,
- (iii)
- The difference between the built-up area under the potential threshold and the built-up area of Changchun based on the statistics were compared and the new threshold was reset until and are sufficiently close. At this point, the difference between and under is,
3.3. Kernel Density Analysis
3.4. Variable Normalization
3.5. Data Gridding
3.6. Two-Factor Combination Mapping
4. Results and Analysis
4.1. Overall Distribution
4.2. Comparison of Spatial Coupling Relationships
4.2.1. POI and NTL Coupling Relationship Is the Same
4.2.2. POI Is Higher Than NTL
4.2.3. POI Is Lower Than NTL
5. Conclusions
- (1)
- Spatial analysis in this study reveals significant spatial coupling between POI and NTL in Changchun in March 2022. Specifically, 84.58% of areas exhibit consistent coupling relationships (High–High, Medium–Medium, Low–Low), indicating a high spatial similarity between the datasets. The distribution, observed as a circular pattern around administrative district built-up areas, suggests a strong link between urban development and human activity patterns. Overall, these findings underscore the high complementarity of POI and NTL in analyzing urban spatial structure distribution.
- (2)
- Further analysis demonstrates that areas with diverse coupling relationships between POI and NTL offer a detailed characterization of urban structure spatial distribution. Clear identification of multiple central gathering areas is possible, with NTL indicating activity intensity but lacking specificity in function. Conversely, POI, concentrated in administrative district centers, better reflects commerce, industry, services, and entertainment. Thus, POI exhibits distinct spatial coupling characteristics compared to NTL.
- (3)
- After two zoning adjustments, Changchun’s spatial structure displays a polycentric nature. As the capital of Jilin Province, the main city encompasses most highly coupled areas, with each surrounding administrative district hosting a highly coupled urban center. However, peripheral areas, compared to the original administrative area, exhibit lower development levels, signaling a need for further development and planning.
- (4)
- Integrating POI and nighttime light data for built-up area extraction facilitates effective mapping of urban spatial structure and reveals development trends. Changchun’s development pattern, as a provincial capital, offers insights applicable to land planning in other Northeastern provincial capitals, highlighting the study’s generalizability.
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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Data Name | Resource |
---|---|
Basic geographic data | National Geomatics Center of China (http://www.ngcc.cn/ngcc/, accessed on 21 January 2023) |
NPP/VIIRS NTL data | The official website of the University of Colorado (https://eogdata.mines.edu/, accessed on 18 November 2022) |
POI | Gaode Open Platform (https://lbs.amap.com/tools/picker, accessed on 15 Febrary 2023) |
DEM | National Catalogue Service for Geographic Information (http://www.webmap.cn, accessed on 18 November 2022) |
Major Categories | Quantity | Percentage/% |
---|---|---|
Spending on Shopping | 62,200 | 26.88% |
Dining and Gourmet | 42,050 | 18.17% |
Life Services | 28,697 | 12.40% |
Company Enterprise | 17,806 | 7.69% |
Transportation Facilities | 16,802 | 7.26% |
Healthcare | 15,468 | 6.68% |
Automotive-Related | 14,478 | 6.26% |
Science, Education, and Culture | 12,568 | 5.43% |
Business Residence | 6163 | 2.66% |
Hotel Accommodation | 5272 | 2.28% |
Financial institutions | 4015 | 1.74% |
Recreation | 2461 | 1.06% |
Sports and Fitness | 2258 | 0.98% |
Tourist Attractions | 1167 | 0.50% |
Coupling Relationship | Number of Pixels | Percentage |
---|---|---|
High–High | 88 | 0.09% |
Medium–Medium | 1823 | 1.81% |
Low–Low | 83,195 | 82.68% |
High–Medium | 676 | 0.67% |
High–Low | 890 | 0.88% |
Medium–Low | 2433 | 2.42% |
Medium–high | 1218 | 1.21% |
Low–High | 4821 | 4.79% |
Low–Medium | 5476 | 5.44% |
Total | 100,620 | 100.00% |
Administrative Region | Gross Regional Product (Billion Yuan) | Population (10,000 People) | Fiscal Revenue (Billion Yuan) |
---|---|---|---|
Nanguan | 445.3 | 48.8 | 60.2 |
Chaoyang | 727.5 | 59.2 | 64.8 |
Kuancheng | 306.2 | 38.8 | 37.5 |
Erdao | 213.3 | 33.0 | 32.4 |
Lyuyuan | 285.6 | 42.7 | 49.6 |
Shuangyang | 155.8 | 36.1 | 16.1 |
Jiutai | 236.5 | 72.0 | 20.8 |
Nong’an | 292.3 | 111.7 | 21.2 |
Yushu | 269.4 | 120.5 | 12.2 |
Dehui | 249.3 | 87.0 | 16.4 |
Gongzhuling | 336.3 | 101.4 | 29.4 |
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Wu, Z.; Wei, X.; He, X.; Gao, W. Identifying Urban Built-Up Areas Based on Spatial Coupling between Nighttime Light Data and POI: A Case Study of Changchun. Buildings 2024, 14, 19. https://doi.org/10.3390/buildings14010019
Wu Z, Wei X, He X, Gao W. Identifying Urban Built-Up Areas Based on Spatial Coupling between Nighttime Light Data and POI: A Case Study of Changchun. Buildings. 2024; 14(1):19. https://doi.org/10.3390/buildings14010019
Chicago/Turabian StyleWu, Ziting, Xindong Wei, Xiujuan He, and Weijun Gao. 2024. "Identifying Urban Built-Up Areas Based on Spatial Coupling between Nighttime Light Data and POI: A Case Study of Changchun" Buildings 14, no. 1: 19. https://doi.org/10.3390/buildings14010019