{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,1]],"date-time":"2026-02-01T01:49:12Z","timestamp":1769910552407,"version":"3.49.0"},"reference-count":83,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2024,1,8]],"date-time":"2024-01-08T00:00:00Z","timestamp":1704672000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Third Comprehensive Scientific Investigation in Xinjiang","award":["2021xjkk1001"],"award-info":[{"award-number":["2021xjkk1001"]}]},{"name":"Third Comprehensive Scientific Investigation in Xinjiang","award":["22BJL061"],"award-info":[{"award-number":["22BJL061"]}]},{"name":"Third Comprehensive Scientific Investigation in Xinjiang","award":["21AZD008"],"award-info":[{"award-number":["21AZD008"]}]},{"name":"Third Comprehensive Scientific Investigation in Xinjiang","award":["41461035"],"award-info":[{"award-number":["41461035"]}]},{"name":"Program of National Social Science Foundation of China","award":["2021xjkk1001"],"award-info":[{"award-number":["2021xjkk1001"]}]},{"name":"Program of National Social Science Foundation of China","award":["22BJL061"],"award-info":[{"award-number":["22BJL061"]}]},{"name":"Program of National Social Science Foundation of China","award":["21AZD008"],"award-info":[{"award-number":["21AZD008"]}]},{"name":"Program of National Social Science Foundation of China","award":["41461035"],"award-info":[{"award-number":["41461035"]}]},{"name":"Major Project of Xinjiang Social Science Foundation","award":["2021xjkk1001"],"award-info":[{"award-number":["2021xjkk1001"]}]},{"name":"Major Project of Xinjiang Social Science Foundation","award":["22BJL061"],"award-info":[{"award-number":["22BJL061"]}]},{"name":"Major Project of Xinjiang Social Science Foundation","award":["21AZD008"],"award-info":[{"award-number":["21AZD008"]}]},{"name":"Major Project of Xinjiang Social Science Foundation","award":["41461035"],"award-info":[{"award-number":["41461035"]}]},{"name":"National Natural Science Foundation of China","award":["2021xjkk1001"],"award-info":[{"award-number":["2021xjkk1001"]}]},{"name":"National Natural Science Foundation of China","award":["22BJL061"],"award-info":[{"award-number":["22BJL061"]}]},{"name":"National Natural Science Foundation of China","award":["21AZD008"],"award-info":[{"award-number":["21AZD008"]}]},{"name":"National Natural Science Foundation of China","award":["41461035"],"award-info":[{"award-number":["41461035"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>In regional development studies, GDP serves as an important indicator for evaluating the developing levels of a region. However, due to statistical methods and possible human-induced interfering factors, GDP is also a commonly criticized indicator for less accurately assessing regional economic development in a dynamic environment, especially during a globalized era. Moreover, common data collection approaches are often challenging to obtain in real-time, and the assessments are prone to inaccuracies. This is especially true in economically underdeveloped regions where data are often less frequently or accurately collected. In recent years, Nighttime Light (NTL) data have emerged as a crucial supplementary data source for regional economic development evaluation and analysis. We adapt this approach and attempt to integrate multiple sources of spatial data to provide a new perspective and more effective tools for economic development evaluation. In our current study, we explore the integration of OpenStreetMap (OSM) data and NTL data in regional studies, and apply a Geographically and Temporally Weighted Regression model (GTWR) for modeling and evaluating regional economic development. Our results suggest that: (1) when using OSM data as a single data source for economic development evaluation, the adjusted R2 value is 0.889. When using NTL data as a single data source for economic development evaluation, the adjusted R2 value is 0.911. However, the fitting performance of OSM data with GDP shows a gradual improvement over time, while the fitting performance of NTL data exhibits a gradual decline starting from the year 2014; (2) Among the economic evaluation models, the GTWR model demonstrates the highest accuracy with an AICc value of 49,112.71, which is 2750.94 lower than the ordinary least squares (OLS) model; (3) The joint modeling of OSM data with NTL data yields an adjusted R2 value of 0.956, which is higher than using either one of them alone. Moreover, this joint modeling approach demonstrates excellent fitting performance, particularly in economically underdeveloped regions, providing a potential alternative for development evaluation in data-poor regions.<\/jats:p>","DOI":"10.3390\/rs16020239","type":"journal-article","created":{"date-parts":[[2024,1,8]],"date-time":"2024-01-08T05:21:38Z","timestamp":1704691298000},"page":"239","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["Exploring the Potential of OpenStreetMap Data in Regional Economic Development Evaluation Modeling"],"prefix":"10.3390","volume":"16","author":[{"given":"Zhe","family":"Wang","sequence":"first","affiliation":[{"name":"College of Geographical and Remote Sensing Science, Xinjiang University, Urumqi 830046, China"}]},{"given":"Jianghua","family":"Zheng","sequence":"additional","affiliation":[{"name":"College of Geographical and Remote Sensing Science, Xinjiang University, Urumqi 830046, China"},{"name":"Key Lab of Smart City and Environmental Modelling, Xinjiang University, Urumqi 830046, China"}]},{"given":"Chuqiao","family":"Han","sequence":"additional","affiliation":[{"name":"College of Geographical and Remote Sensing Science, Xinjiang University, Urumqi 830046, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7847-7560","authenticated-orcid":false,"given":"Binbin","family":"Lu","sequence":"additional","affiliation":[{"name":"College of Geographical and Remote Sensing Science, Xinjiang University, Urumqi 830046, China"},{"name":"School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4470-7011","authenticated-orcid":false,"given":"Danlin","family":"Yu","sequence":"additional","affiliation":[{"name":"Department of Earth and Environmental Studies, Montclair State University, Montclair, NJ 07043, USA"}]},{"given":"Juan","family":"Yang","sequence":"additional","affiliation":[{"name":"College of Geographical and Remote Sensing Science, Xinjiang University, Urumqi 830046, China"}]},{"given":"Linzhi","family":"Han","sequence":"additional","affiliation":[{"name":"School of Economics and Management, Xinjiang University, Urumqi 830046, China"}]}],"member":"1968","published-online":{"date-parts":[[2024,1,8]]},"reference":[{"key":"ref_1","unstructured":"(2023, September 08). United Nations The Sustainable Development Agenda\u2014United Nations Sustainable Development. Available online: https:\/\/www.un.org\/sustainabledevelopment\/development-agenda\/."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"994","DOI":"10.1257\/aer.102.2.994","article-title":"Measuring Economic Growth from Outer Space","volume":"102","author":"Henderson","year":"2012","journal-title":"Am. Econ. Rev."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Chen, X., and Nordhaus, W.D. (2019). VIIRS Nighttime Lights in the Estimation of Cross-Sectional and Time-Series GDP. Remote Sens., 11.","DOI":"10.3390\/rs11091057"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"052060","DOI":"10.1088\/1755-1315\/310\/5\/052060","article-title":"Construction of Regional Economic Development Model Based on Remote Sensing Data","volume":"310","author":"Gu","year":"2019","journal-title":"IOP Conf. Ser. Earth Environ. Sci."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Bartik, T. (2002). Evaluating the Impacts of Local Economic Development Policies on Local Economic Outcomes: What Has Been Done and What Is Doable?, W.E. Upjohn Institute for Employment Research.","DOI":"10.17848\/wp03-89"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"102565","DOI":"10.1016\/j.irfa.2023.102565","article-title":"Labor Education, Cash Transfers and Student Development: Evidence from China","volume":"87","author":"Li","year":"2023","journal-title":"Int. Rev. Financ. Anal."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"107437","DOI":"10.1016\/j.ecolecon.2022.107437","article-title":"Challenges and Innovations in the Economic Evaluation of the Risks of Climate Change","volume":"197","author":"Rising","year":"2022","journal-title":"Ecol. Econ."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"101643","DOI":"10.1016\/j.chieco.2021.101643","article-title":"China\u2019s Poverty Reduction Miracle and Relative Poverty: Focusing on the Roles of Growth and Inequality","volume":"68","author":"Wan","year":"2021","journal-title":"China Econ. Rev."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"348","DOI":"10.1016\/j.ecolind.2018.02.005","article-title":"A Three-Dimensional Evaluation Model for Regional Carrying Capacity of Ecological Environment to Social Economic Development: Model Development and a Case Study in China","volume":"89","author":"Wang","year":"2018","journal-title":"Ecol. Indic."},{"key":"ref_10","first-page":"243","article-title":"The Code of Targeted Poverty Alleviation in China: A Geography Perspective","volume":"2","author":"Yang","year":"2021","journal-title":"Geogr. Sustain."},{"key":"ref_11","first-page":"45","article-title":"Regional disparities of urbanization levels in China","volume":"1","author":"Jiang","year":"2001","journal-title":"Chin. J. Popul. Sci."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1876","DOI":"10.1177\/2399808320951580","article-title":"Mapping China\u2019s Regional Economic Activity by Integrating Points-of-Interest and Remote Sensing Data with Random Forest","volume":"48","author":"Chen","year":"2021","journal-title":"Environ. Plan. B Urban Anal. City Sci."},{"key":"ref_13","first-page":"257","article-title":"Limitations of the GDP as a measure of progress and well-being","volume":"29","year":"2016","journal-title":"Ekonomski Vjesnik"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"851","DOI":"10.1111\/polp.12277","article-title":"Poor Quality of Data in Africa: What Are the Issues?","volume":"46","author":"Kinyondo","year":"2018","journal-title":"Politics Policy"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"416","DOI":"10.1016\/j.jpolmod.2019.02.013","article-title":"A Remote Sensing Look at the Economy of a Russian Region (Rostov) Adjacent to the Ukrainian Crisis","volume":"41","author":"Brock","year":"2019","journal-title":"J. Policy Model."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"102081","DOI":"10.1016\/j.apgeog.2019.102081","article-title":"Exploring the Spatial Differentiation of Urbanization on Two Sides of the Hu Huanyong Line\u2014Based on Nighttime Light Data and Cellular Automata","volume":"112","author":"Chen","year":"2019","journal-title":"Appl. Geogr."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"3590","DOI":"10.1080\/10106049.2020.1870166","article-title":"Poverty Estimation at the County Level by Combining LuoJia1-01 Nighttime Light Data and Points of Interest","volume":"37","author":"Lin","year":"2022","journal-title":"Geocarto Int."},{"key":"ref_18","first-page":"103375","article-title":"Multi-Scale Estimation of Poverty Rate Using Night-Time Light Imagery","volume":"121","author":"Shao","year":"2023","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"102899","DOI":"10.1016\/j.apgeog.2023.102899","article-title":"Nighttime Light Satellite Images Reveal Uneven Socioeconomic Development along China\u2019s Land Border","volume":"152","author":"Wan","year":"2023","journal-title":"Appl. Geogr."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.isprsjprs.2018.08.018","article-title":"Tweets or Nighttime Lights: Comparison for Preeminence in Estimating Socioeconomic Factors","volume":"146","author":"Zhao","year":"2018","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Cao, C., Zhang, B., Xia, F., and Bai, Y. (2022). Exploring VIIRS Night Light Long-Term Time Series with CNN\/SI for Urban Change Detection and Aerosol Monitoring. Remote Sens., 14.","DOI":"10.3390\/rs14133126"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"317","DOI":"10.1016\/j.rse.2019.03.013","article-title":"Detecting and Monitoring Long-Term Landslides in Urbanized Areas with Nighttime Light Data and Multi-Seasonal Landsat Imagery across Taiwan from 1998 to 2017","volume":"225","author":"Chen","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"102602","DOI":"10.1016\/j.jdeveco.2020.102602","article-title":"Which Night Lights Data Should We Use in Economics, and Where?","volume":"149","author":"Gibson","year":"2021","journal-title":"J. Dev. Econ."},{"key":"ref_24","first-page":"1348","article-title":"Development Characteristics Estimation of Shandong Peninsula Urban Agglomeration Using VIIRS Night Light Data","volume":"35","author":"Li","year":"2021","journal-title":"Remote Sens. Technol. Appl."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"3057","DOI":"10.3390\/rs5063057","article-title":"Potential of NPP-VIIRS Nighttime Light Imagery for Modeling the Regional Economy of China","volume":"5","author":"Li","year":"2013","journal-title":"Remote Sens."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"42","DOI":"10.5958\/2249-7137.2022.00183.5","article-title":"Observing Economics through Geography: COVID-19 and Night-Light Data Analysis of Bangladesh and Sri Lanka (2017\u20132021)","volume":"12","author":"Puri","year":"2022","journal-title":"ACADEMICIA Int. Multidiscip. Res. J."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"239","DOI":"10.1016\/j.apgeog.2017.12.012","article-title":"Modeling Population Density Based on Nighttime Light Images and Land Use Data in China","volume":"90","author":"Tan","year":"2018","journal-title":"Appl. Geogr."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"509","DOI":"10.1016\/S0921-8009(02)00097-6","article-title":"Global Estimates of Market and Non-Market Values Derived from Nighttime Satellite Imagery, Land Cover, and Ecosystem Service Valuation","volume":"41","author":"Sutton","year":"2002","journal-title":"Ecol. Econ."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Wang, X., Rafa, M., Moyer, J.D., Li, J., Scheer, J., and Sutton, P. (2019). Estimation and Mapping of Sub-National GDP in Uganda Using NPP-VIIRS Imagery. Remote Sens., 11.","DOI":"10.3390\/rs11020163"},{"key":"ref_30","first-page":"591","article-title":"An Overview on Data Mining of Nighttime Light Remote Sensing","volume":"44","author":"Li","year":"2015","journal-title":"Acta Geod. Et Cartogr. Sin."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Li, C., Huo, Z., Wang, X., and Wu, Y. (2022). Study on Spatio-Temporal Modelling between NPP-VIIRS Night-Time Light Intensity and GDP for Major Urban Agglomerations in China. Int. J. Remote Sens., 1\u201324.","DOI":"10.1080\/01431161.2022.2133580"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"889","DOI":"10.5194\/essd-13-889-2021","article-title":"An Extended Time Series (2000\u20132018) of Global NPP-VIIRS-like Nighttime Light Data from a Cross-Sensor Calibration","volume":"13","author":"Chen","year":"2021","journal-title":"Earth Syst. Sci. Data"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"481","DOI":"10.1016\/j.asr.2019.09.035","article-title":"GDP Spatialization in Ningbo City Based on NPP\/VIIRS Night-Time Light and Auxiliary Data Using Random Forest Regression","volume":"65","author":"Liang","year":"2020","journal-title":"Adv. Space Res."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"1705","DOI":"10.3390\/rs6021705","article-title":"Evaluating the Ability of NPP-VIIRS Nighttime Light Data to Estimate the Gross Domestic Product and the Electric Power Consumption of China at Multiple Scales: A Comparison with DMSP-OLS Data","volume":"6","author":"Shi","year":"2014","journal-title":"Remote Sens."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"111357","DOI":"10.1016\/j.rse.2019.111357","article-title":"Anisotropic Characteristic of Artificial Light at Night\u2013Systematic Investigation with VIIRS DNB Multi-Temporal Observations","volume":"233","author":"Li","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"211","DOI":"10.1007\/s10708-007-9111-y","article-title":"Citizens as Sensors: The World of Volunteered Geography","volume":"69","author":"Goodchild","year":"2007","journal-title":"GeoJournal"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"See, L., Mooney, P., Foody, G., Bastin, L., Comber, A., Estima, J., Fritz, S., Kerle, N., Jiang, B., and Laakso, M. (2016). Crowdsourcing, Citizen Science or Volunteered Geographic Information? The Current State of Crowdsourced Geographic Information. ISPRS Int. J. Geo-Inf., 5.","DOI":"10.3390\/ijgi5050055"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"110","DOI":"10.1016\/j.spasta.2012.03.002","article-title":"Assuring the Quality of Volunteered Geographic Information","volume":"1","author":"Goodchild","year":"2012","journal-title":"Spat. Stat."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"287","DOI":"10.1016\/j.jce.2008.02.003","article-title":"Measuring Underground (Unobserved, Non-Observed, Unrecorded) Economies in Transition Countries: Can We Trust GDP?","volume":"36","author":"Feige","year":"2008","journal-title":"J. Comp. Econ."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Jokar Arsanjani, J., Zipf, A., Mooney, P., and Helbich, M. (2015). OpenStreetMap in GIScience: Experiences, Research, and Applications, Springer International Publishing. Lecture Notes in Geoinformation and Cartography.","DOI":"10.1007\/978-3-319-14280-7"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"76","DOI":"10.3390\/fi6010076","article-title":"Recent Developments and Future Trends in Volunteered Geographic Information Research: The Case of OpenStreetMap","volume":"6","author":"Neis","year":"2014","journal-title":"Future Internet"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Bandrova, T., Konecny, M., and Zlatanova, S. (2014). Thematic Cartography for the Society, Springer International Publishing. Lecture Notes in Geoinformation and Cartography.","DOI":"10.1007\/978-3-319-08180-9"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"178","DOI":"10.1139\/geomat-2021-0012","article-title":"Exploring Five Indicators for the Quality of OpenStreetMap Road Networks: A Case Study of Qu\u00e9bec, Canada","volume":"75","author":"Moradi","year":"2022","journal-title":"Geomatica"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"1941","DOI":"10.1073\/pnas.1905232116","article-title":"Global Trends toward Urban Street-Network Sprawl","volume":"117","year":"2020","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Hadimlioglu, I.A., and King, S.A. (2019). City Maker: Reconstruction of Cities from OpenStreetMap Data for Environmental Visualization and Simulations. ISPRS Int. J. Geo-Inf., 8.","DOI":"10.3390\/ijgi8070298"},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Zhang, L., and Pfoser, D. (2019). Using OpenStreetMap Point-of-Interest Data to Model Urban Change\u2014A Feasibility Study. PLoS ONE, 14.","DOI":"10.1371\/journal.pone.0212606"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"3037","DOI":"10.1038\/s41598-021-82404-z","article-title":"The Evolution of Humanitarian Mapping within the OpenStreetMap Community","volume":"11","author":"Herfort","year":"2021","journal-title":"Sci. Rep."},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Liu, B., Shi, Y., Li, D.-J., Wang, Y.-D., Fernandez, G., and Tsou, M.-H. (2020). An Economic Development Evaluation Based on the OpenStreetMap Road Network Density: The Case Study of 85 Cities in China. ISPRS Int. J. Geo-Inf., 9.","DOI":"10.3390\/ijgi9090517"},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Borkowska, S., and Pokonieczny, K. (2022). Analysis of OpenStreetMap Data Quality for Selected Counties in Poland in Terms of Sustainable Development. Sustainability, 14.","DOI":"10.3390\/su14073728"},{"key":"ref_50","unstructured":"Budhathoki, N.R. (2010). Participants\u2019 Motivations to Contribute Geographic Information in an Online Community, University of Illinois at Urbana-Champaign."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"2587","DOI":"10.1109\/JSTARS.2018.2844566","article-title":"Urban Land Extraction Using DMSP\/OLS Nighttime Light Data and OpenStreetMap Datasets for Cities in China at Different Development Levels","volume":"11","author":"Cheng","year":"2018","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Shi, L., and Ling, F. (2021). Local Climate Zone Mapping Using Multi-Source Free Available Datasets on Google Earth Engine Platform. Land, 10.","DOI":"10.3390\/land10050454"},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"8566","DOI":"10.1080\/01431161.2019.1615655","article-title":"An Estimation of Housing Vacancy Rate Using NPP-VIIRS Night-Time Light Data and OpenStreetMap Data","volume":"40","author":"Wang","year":"2019","journal-title":"Int. J. Remote Sens."},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Ma, D., Guo, R., Jing, Y., Zheng, Y., Zhao, Z., and Yang, J. (2021). Intra-Urban Scaling Properties Examined by Automatically Extracted City Hotspots from Street Data and Nighttime Light Imagery. Remote Sens., 13.","DOI":"10.3390\/rs13071322"},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"417","DOI":"10.1080\/13658810902832591","article-title":"Detecting Negative Spatial Autocorrelation in Georeferenced Random Variables","volume":"24","author":"Griffith","year":"2010","journal-title":"Int. J. Geogr. Inf. Sci."},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Fischer, M.M., and Getis, A. (2010). Handbook of Applied Spatial Analysis: Software Tools, Methods and Applications, Springer.","DOI":"10.1007\/978-3-642-03647-7"},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"1121","DOI":"10.1016\/j.egypro.2018.09.136","article-title":"The Spatial Characteristics of Coupling Relationship between Urbanization and Eco-Environment in the Pan Yangtze River Delta","volume":"152","author":"Wu","year":"2018","journal-title":"Energy Procedia"},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"2941","DOI":"10.1007\/s10668-019-00328-1","article-title":"Manufacturing Industry Agglomeration and Spatial Clustering: Evidence from Hebei Province, China","volume":"22","author":"Li","year":"2020","journal-title":"Environ. Dev. Sustain."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"17","DOI":"10.1093\/biomet\/37.1-2.17","article-title":"Notes on Continuous Stochastic Phenomena","volume":"37","author":"Moran","year":"1950","journal-title":"Biometrika"},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"93","DOI":"10.1111\/j.1538-4632.1995.tb00338.x","article-title":"Local Indicators of Spatial Association\u2014LISA","volume":"27","author":"Anselin","year":"1995","journal-title":"Geogr. Anal."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"286","DOI":"10.1111\/j.1538-4632.1995.tb00912.x","article-title":"Local Spatial Autocorrelation Statistics: Distributional Issues and an Application","volume":"27","author":"Ord","year":"1995","journal-title":"Geogr. Anal."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"5105","DOI":"10.1038\/s41598-022-07656-9","article-title":"Water Ecological Security Assessment and Spatial Autocorrelation Analysis of Prefectural Regions Involved in the Yellow River Basin","volume":"12","author":"Qiu","year":"2022","journal-title":"Sci. Rep."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"383","DOI":"10.1080\/13658810802672469","article-title":"Geographically and Temporally Weighted Regression for Modeling Spatio-Temporal Variation in House Prices","volume":"24","author":"Huang","year":"2010","journal-title":"Int. J. Geogr. Inf. Sci."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"195","DOI":"10.1007\/s10708-013-9500-3","article-title":"Understanding Regional Development Mechanisms in Greater Beijing Area, China, 1995\u20132001, from a Spatial\u2013Temporal Perspective","volume":"79","author":"Yu","year":"2014","journal-title":"GeoJournal"},{"key":"ref_65","first-page":"160","article-title":"Challenging the Current Measurement of China\u2019s Provincial Total Factor Productivity: A Spatial Econometric Perspective","volume":"11","author":"Yu","year":"2009","journal-title":"China Soft Sci."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"103836","DOI":"10.1016\/j.scs.2022.103836","article-title":"Spatio-Temporal Evolution Relationships between Provincial CO2 Emissions and Driving Factors Using Geographically and Temporally Weighted Regression Model","volume":"81","author":"Li","year":"2022","journal-title":"Sustain. Cities Soc."},{"key":"ref_67","doi-asserted-by":"crossref","unstructured":"Parzen, E., Tanabe, K., and Kitagawa, G. (1998). Selected Papers of Hirotugu Akaike, Springer.","DOI":"10.1007\/978-1-4612-1694-0"},{"key":"ref_68","first-page":"33","article-title":"The Distribution of Population in China, with Statistics and Maps","volume":"2","author":"Hu","year":"1935","journal-title":"Acta Geogr. Sin."},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"292","DOI":"10.1016\/j.apgeog.2010.06.003","article-title":"Detecting Spatially Non-Stationary and Scale-Dependent Relationships between Urban Landscape Fragmentation and Related Factors Using Geographically Weighted Regression","volume":"31","author":"Gao","year":"2011","journal-title":"Appl. Geogr."},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"1018","DOI":"10.1111\/j.1600-0587.2011.06763.x","article-title":"Isolation Determines Patterns of Species Presence in Highly Fragmented Landscapes","volume":"34","author":"Boscolo","year":"2011","journal-title":"Ecography"},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"170","DOI":"10.1016\/j.ecolind.2012.02.007","article-title":"Spatial Pattern of Non-Stationarity and Scale-Dependent Relationships between NDVI and Climatic Factors\u2014A Case Study in Qinghai-Tibet Plateau, China","volume":"20","author":"Gao","year":"2012","journal-title":"Ecol. Indic."},{"key":"ref_72","unstructured":"Auxier, B., and Anderson, M. (2021). Social Media Use in 2021, Pew Research Center."},{"key":"ref_73","first-page":"868","article-title":"Measuring the Gender Gap on the Internet","volume":"81","author":"Bimber","year":"2000","journal-title":"Soc. Sci. Q."},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"590","DOI":"10.1177\/0002764204271508","article-title":"Age, Ethnicity, and Socioeconomic Patterns in Early Computer Use: A National Survey","volume":"48","author":"Calvert","year":"2005","journal-title":"Am. Behav. Sci."},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"8535","DOI":"10.1109\/JSTARS.2023.3312508","article-title":"Exploring the Characteristics and Drivers of Expansion in the Shandong Peninsula Urban Agglomeration Based on Nighttime Light Data","volume":"16","author":"Song","year":"2023","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"103851","DOI":"10.1016\/j.scs.2022.103851","article-title":"Spatial Mismatches between Nighttime Light Intensity and Building Morphology in Shanghai, China","volume":"81","author":"Xu","year":"2022","journal-title":"Sustain. Cities Soc."},{"key":"ref_77","doi-asserted-by":"crossref","first-page":"146586","DOI":"10.1016\/j.scitotenv.2021.146586","article-title":"Using Nighttime Light Data to Identify the Structure of Polycentric Cities and Evaluate Urban Centers","volume":"780","author":"Yang","year":"2021","journal-title":"Sci. Total Environ."},{"key":"ref_78","doi-asserted-by":"crossref","first-page":"1237","DOI":"10.5194\/isprs-archives-XLII-2-W7-1237-2017","article-title":"Night time light satellite data for evaluating the socioeconomics in central Asia","volume":"42","author":"Li","year":"2017","journal-title":"Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci."},{"key":"ref_79","doi-asserted-by":"crossref","first-page":"129558","DOI":"10.1016\/j.jclepro.2021.129558","article-title":"Evaluating the Performance of LBSM Data to Estimate the Gross Domestic Product of China at Multiple Scales: A Comparison with NPP-VIIRS Nighttime Light Data","volume":"328","author":"Huang","year":"2021","journal-title":"J. Clean. Prod."},{"key":"ref_80","doi-asserted-by":"crossref","first-page":"1906","DOI":"10.1109\/LGRS.2020.3010936","article-title":"Identifying and Evaluating the Nighttime Economy in China Using Multisource Data","volume":"18","author":"Cui","year":"2021","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_81","doi-asserted-by":"crossref","unstructured":"Janelle, D.G., Warf, B., and Hansen, K. (2004). WorldMinds: Geographical Perspectives on 100 Problems: Commemorating the 100th Anniversary of the Association of American Geographers 1904\u20132004, Springer.","DOI":"10.1007\/978-1-4020-2352-1"},{"key":"ref_82","doi-asserted-by":"crossref","first-page":"394","DOI":"10.1177\/0047287520987627","article-title":"Measuring Tourism Intensification in Urban Destinations: An Approach Based on Fractal Analysis","volume":"61","author":"Ferreira","year":"2022","journal-title":"J. Travel Res."},{"key":"ref_83","doi-asserted-by":"crossref","first-page":"281","DOI":"10.1016\/j.landurbplan.2017.05.008","article-title":"Understanding Uneven Urban Expansion with Natural Cities Using Open Data","volume":"177","author":"Long","year":"2018","journal-title":"Landsc. Urban Plan."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/2\/239\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T13:41:53Z","timestamp":1760103713000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/2\/239"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,1,8]]},"references-count":83,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2024,1]]}},"alternative-id":["rs16020239"],"URL":"https:\/\/doi.org\/10.3390\/rs16020239","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,1,8]]}}}