213 citations as recorded by crossref.

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4. Combining Conventional Statistics and Big Data to Map Global Tourism Destinations Before COVID-19 C. Adamiak & B. Szyda 10.1177/00472875211051418
5. The Impact of Spatial Resolutions on Nature-Based Solution Suitability Mapping for Europe J. Ommer et al. 10.3390/app14114608
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14. Comparative Study of Small-Area Population Prediction Methods, GWR, and CCR: A Case Study of Wuhan, China H. Xiang et al. 10.1061/JUPDDM.UPENG-4922
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20. Current and future rainfall-driven flood risk from hurricanes in Puerto Rico under 1.5 and 2 °C climate change L. Archer et al. 10.5194/nhess-24-375-2024
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23. Estimating the Population of a Middle Eastern City Based on a Normalized Difference Built-Up Index and Urban Morphology E. Alyasiri et al. 10.4018/IJAGR.313942
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26. Predicting Near-Future Built-Settlement Expansion Using Relative Changes in Small Area Populations J. Nieves et al. 10.3390/rs12101545
27. Assessing variations in land cover-land use and surface temperature dynamics for Dehradun, India, using multi-time and multi-sensor landsat data K. Mishra & R. Garg 10.1007/s10661-023-10945-z
28. Identification of Industrial Land Parcels and Its Implications for Environmental Risk Management in the Beijing–Tianjin–Hebei Urban Agglomeration Z. Wang et al. 10.3390/su12010174
29. Advances on the Global Human Settlement Layer by joint assessment of Earth Observation and population survey data M. Pesaresi et al. 10.1080/17538947.2024.2390454
30. Unraveling Long-Term Flood Risk Dynamics Across the Murray-Darling Basin Using a Large-Scale Hydraulic Model and Satellite Data S. Ceola et al. 10.3389/frwa.2021.797259
31. Multi-Scale Dynamics and Spatial Consistency of Economy and Population Based on NPP/VIIRS Nighttime Light Data and Population Imagery: A Case Study of the Yangtze River Delta Y. Xu et al. 10.3390/rs16152806
32. First year with COVID-19: Assessment and prospects R. Bergquist et al. 10.4081/gh.2020.953
33. Population, land use and economic exposure estimates for Europe at 100 m resolution from 1870 to 2020 D. Paprotny & M. Mengel 10.1038/s41597-023-02282-0
34. Global population datasets overestimate flood exposure in Sweden K. Karagiorgos et al. 10.1038/s41598-024-71330-5
35. A census from heaven: Unraveling the potential of deep learning and Earth Observation for intra-urban population mapping in data scarce environments S. Georganos et al. 10.1016/j.jag.2022.103013
36. Geospatial analysis of food waste generation at the consumer-level in high-income regions, 2000-2023 – A scoping review V. Thaore et al. 10.1016/j.envres.2024.120247
37. High-resolution population mapping based on SDGSAT-1 glimmer imagery and deep learning: a case study of the Guangdong-Hong Kong-Marco Greater Bay Area H. Duan et al. 10.1080/17538947.2024.2407519
38. Fine-grained, spatiotemporal datasets measuring 200 years of land development in the United States J. Uhl et al. 10.5194/essd-13-119-2021
39. Disaggregating Population Data and Evaluating the Accuracy of Modeled High-Resolution Population Distribution—The Case Study of Germany S. Eichhorn 10.3390/su12103976
40. Looking Back, Looking Forward: Progress and Prospect for Spatial Demography S. Matthews et al. 10.1007/s40980-021-00084-9
41. Urban ecosystem services supply-demand assessment from the perspective of the water-energy-food nexus T. Ding et al. 10.1016/j.scs.2023.104401
42. Mapping hourly population dynamics using remotely sensed and geospatial data: a case study in Beijing, China X. Zhao et al. 10.1080/15481603.2021.1935128
43. Disaggregating population data for assessing progress of SDGs: methods and applications Y. Qiu et al. 10.1080/17538947.2021.2013553
44. Assessing the relative accuracy of gridded population sampling: results from an election survey experiment S. Ford & M. Kirwin 10.1080/13645579.2022.2091200
45. GHS-POP Accuracy Assessment: Poland and Portugal Case Study B. Calka & E. Bielecka 10.3390/rs12071105
46. Combining expert and crowd-sourced training data to map urban form and functions for the continental US M. Demuzere et al. 10.1038/s41597-020-00605-z
47. High-resolution population estimation using household survey data and building footprints G. Boo et al. 10.1038/s41467-022-29094-x
48. The Missing Millions in Maps: Exploring Causes of Uncertainties in Global Gridded Population Datasets M. Kuffer et al. 10.3390/ijgi11070403
49. Exploring spatial feedbacks between adaptation policies and internal migration patterns due to sea-level rise L. Reimann et al. 10.1038/s41467-023-38278-y
50. Improvement of a Dasymetric Method for Implementing Sustainable Development Goal 11 Indicators at an Intra-Urban Scale M. Aquilino et al. 10.3390/rs13142835
51. Different Spatiotemporal Patterns in Global Human Population and Built‐Up Land J. Gao & B. O'Neill 10.1029/2020EF001920
52. Estimation of pixel-level seismic vulnerability of the building environment based on mid-resolution optical remote sensing images X. Fan et al. 10.1016/j.jag.2021.102339
53. Multiple Global Population Datasets: Differences and Spatial Distribution Characteristics R. Chen et al. 10.3390/ijgi9110637
54. A novel urban population-land coupling simulation model: Integrating spatial equilibrium and macro-micro strategies Y. Guo et al. 10.1016/j.jag.2024.104002
55. Warming-induced cryosphere changes predict drier Andean eco-regions A. Al-Yaari et al. 10.1088/1748-9326/ad6ea6
56. Asset exposure data for global physical risk assessment S. Eberenz et al. 10.5194/essd-12-817-2020
57. Global population profile of tropical cyclone exposure from 2002 to 2019 R. Jing et al. 10.1038/s41586-023-06963-z
58. Spatiotemporal dynamics of population density in China using nighttime light and geographic weighted regression method* W. Guo et al. 10.1080/17538947.2023.2233493
59. Incorporating Geographic Information Science and Technology in Response to the COVID-19 Pandemic C. Smith & J. Mennis 10.5888/pcd17.200246
60. Evaluating spatial statistical and machine learning models in urban dynamic population mapping Z. Cheng et al. 10.1177/27541231221114169
61. Dasymetric population mapping based on US census data and 30-m gridded estimates of impervious surface R. Swanwick et al. 10.1038/s41597-022-01603-z
62. Population development as a driver of coastal risk: Current trends and future pathways L. Reimann et al. 10.1017/cft.2023.3
63. High-Resolution Gridded Population Datasets: Exploring the Capabilities of the World Settlement Footprint 2019 Imperviousness Layer for the African Continent D. Palacios-Lopez et al. 10.3390/rs13061142
64. Experimentelle georeferenzierte Bevölkerungszahl auf Basis der Bevölkerungsfortschreibung und Mobilfunkdaten S. Hadam 10.1007/s11943-023-00320-2
65. Using satellite imagery to understand and promote sustainable development M. Burke et al. 10.1126/science.abe8628
66. Improving intelligent dasymetric mapping population density estimates at 30 m resolution for the conterminous United States by excluding uninhabited areas J. Baynes et al. 10.5194/essd-14-2833-2022
67. Spatial access inequities and childhood immunisation uptake in Kenya N. Joseph et al. 10.1186/s12889-020-09486-8
68. Approaches and tools for user-driven provenance and data quality information in spatial data infrastructures J. Fischer et al. 10.1080/17538947.2023.2198778
69. Global riverine flood risk – how do hydrogeomorphic floodplain maps compare to flood hazard maps? S. Lindersson et al. 10.5194/nhess-21-2921-2021
70. Global Harmonization of Urbanization Measures: Proceed with Care D. Balk et al. 10.3390/rs13244973
71. A pixel level evaluation of five multitemporal global gridded population datasets: a case study in Sweden, 1990–2015 M. Archila Bustos et al. 10.1007/s11111-020-00360-8
72. Climatic Criteria of the Need for Preventive Adaptation A. Ginzburg et al. 10.1134/S0001433822060068
73. Impact of state weights on national vaccination coverage estimates from household surveys in Nigeria T. Dong et al. 10.1016/j.vaccine.2020.05.026
74. Stability and Changes in the Spatial Distribution of China’s Population in the Past 30 Years Based on Census Data Spatialization X. Xu et al. 10.3390/rs15061674
75. Mapping monthly population distribution and variation at 1-km resolution across China Z. Cheng et al. 10.1080/13658816.2020.1854767
76. Remote sensing of urban greenspace exposure and equality: Scaling effects from greenspace and population mapping S. Wu et al. 10.1016/j.ufug.2023.128136
77. High-resolution estimates of social distancing feasibility, mapped for urban areas in sub-Saharan Africa H. Chamberlain et al. 10.1038/s41597-022-01799-0
78. A Global Analysis of the Relationship Between Urbanization and Fatalities in Earthquake-Prone Areas C. He et al. 10.1007/s13753-021-00385-z
79. Global trends and local variations in land take per person M. Li et al. 10.1016/j.landurbplan.2021.104308
80. Spatially explicit downscaling and projection of population in mainland China W. Xu et al. 10.1016/j.scitotenv.2024.173623
81. A grid-based sample design framework for household surveys G. Boo et al. 10.12688/gatesopenres.13107.1
82. Disaggregating census data for population mapping using a Bayesian Additive Regression Tree model O. Yankey et al. 10.1016/j.apgeog.2024.103416
83. Evaluating the Accuracy of Gridded Population Estimates in Slums: A Case Study in Nigeria and Kenya D. Thomson et al. 10.3390/urbansci5020048
84. Human and infrastructure exposure to large wildfires in the United States A. Modaresi Rad et al. 10.1038/s41893-023-01163-z
85. Evaluation of NLDAS-2 and Downscaled Air Temperature data in Florida J. Jung et al. 10.1080/02723646.2021.1928878
86. Spatially explicit reconstruction of the population distribution in the Tuojiang River Basin during 1911–2010 using random forest regression Q. Wang et al. 10.1007/s10113-021-01872-1
87. A simulated ‘sandbox’ for exploring the modifiable areal unit problem in aggregation and disaggregation J. Nieves et al. 10.1038/s41597-024-03061-1
88. Implications for Tracking SDG Indicator Metrics with Gridded Population Data C. Tuholske et al. 10.3390/su13137329
89. Human displacements from Tropical Cyclone Idai attributable to climate change B. Mester et al. 10.5194/nhess-23-3467-2023
90. How accurate are WorldPop-Global-Unconstrained gridded population data at the cell-level?: A simulation analysis in urban Namibia D. Thomson et al. 10.1371/journal.pone.0271504
91. The Unseen Population: Underestimation of Slum Dwellers in Cities of the Global South J. Breuer et al. 10.2139/ssrn.4098362
92. A global comparative study on the impact of COVID-19 policy on atmospheric nitrogen dioxide (NO2): Evidence from remote sensing data in 2019–2022 B. Zhang et al. 10.1016/j.jenvman.2024.121851
93. Cropland abandonment in the context of drought, economic restructuring, and migration in northeast Syria L. Eklund et al. 10.1088/1748-9326/ad1723
94. Global flood exposure from different sized rivers M. Bernhofen et al. 10.5194/nhess-21-2829-2021
95. An ANN-based method for population Dasymetric mapping to avoid the scale heterogeneity: A case study in Hong Kong, 2016–2021 W. Lu & Q. Weng 10.1016/j.compenvurbsys.2024.102072
96. The Ratio of the Land Consumption Rate to the Population Growth Rate: A Framework for the Achievement of the Spatiotemporal Pattern in Poland and Lithuania B. Calka et al. 10.3390/rs14051074
97. Spatially explicit accuracy assessment of deep learning-based, fine-resolution built-up land data in the United States J. Uhl & S. Leyk 10.1016/j.jag.2023.103469
98. Elucidating the impacts of rapid urban expansion on air quality in the Yangtze River Delta, China X. Zhang et al. 10.1016/j.scitotenv.2021.149426
99. Population spatialization with pixel-level attribute grading by considering scale mismatch issue in regression modeling Y. Mei et al. 10.1080/10095020.2021.2021785
100. Emergency flood bulletins for Cyclones Idai and Kenneth: A critical evaluation of the use of global flood forecasts for international humanitarian preparedness and response R. Emerton et al. 10.1016/j.ijdrr.2020.101811
101. The Role of Global Data Sets for Riverine Flood Risk Management at National Scales M. Bernhofen et al. 10.1029/2021WR031555
102. Inter-observer reliability in transect-based observations of environmental waste in greater accra and kisumu: implications for waste management J. Okotto-Okotto et al. 10.1007/s13762-024-05625-5
103. Identifying vulnerable populations and transmission pathways by geographic correlation of the environment to human health T. Price & S. Barua 10.1016/j.scitotenv.2021.146426
104. Spatial aggregation with respect to a population distribution: Impact on inference J. Paige et al. 10.1016/j.spasta.2022.100714
105. Sub-pixel building area mapping based on synthetic training data and regression-based unmixing using Sentinel-1 and -2 data F. Schug et al. 10.1080/2150704X.2022.2088253
106. Walkability der Stadt Regensburg – eine Mixed-methods-Untersuchung mittels QGIS und Walk Audits M. Heudobler et al. 10.1007/s11553-024-01165-5
107. Enhancing population data granularity: A comprehensive approach using LiDAR, POI, and quadratic programming X. Ye et al. 10.1016/j.cities.2024.105223
108. Comparative assessment of gridded population data sets for complex topography: a study of Southwest China Y. Xu et al. 10.1007/s11111-020-00366-2
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110. Monitoring and projecting global hunger: Are we on track? M. Cooper et al. 10.1016/j.gfs.2021.100568
111. Spatial Information Gaps on Deprived Urban Areas (Slums) in Low-and-Middle-Income-Countries: A User-Centered Approach M. Kuffer et al. 10.3390/urbansci5040072
112. Intercensal Updating Using Structure-Preserving Methods and Satellite Imagery T. Koebe et al. 10.1111/rssa.12802
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117. Mapping Deprived Urban Areas Using Open Geospatial Data and Machine Learning in Africa M. Owusu et al. 10.3390/urbansci7040116
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121. Measuring the accuracy of gridded human population density surfaces: A case study in Bioko Island, Equatorial Guinea B. Fries et al. 10.1371/journal.pone.0248646
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123. Dealing with uncertainty in famine predictions: How complex events affect food security early warning skill in the Greater Horn of Africa P. Krishnamurthy et al. 10.1016/j.gfs.2020.100374
124. Flood hazard potential reveals global floodplain settlement patterns L. Devitt et al. 10.1038/s41467-023-38297-9
125. So2Sat POP - A Curated Benchmark Data Set for Population Estimation from Space on a Continental Scale S. Doda et al. 10.1038/s41597-022-01780-x
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130. Spatiotemporal dynamics of global population and heat exposure (2020–2100): based on improved SSP-consistent population projections M. Li et al. 10.1088/1748-9326/ac8755
131. The unseen population: Do we underestimate slum dwellers in cities of the Global South? J. Breuer et al. 10.1016/j.habitatint.2024.103056
132. Human populations in the world’s mountains: Spatio-temporal patterns and potential controls J. Thornton et al. 10.1371/journal.pone.0271466
133. A novel GIS-based multicriteria analysis approach for ascertaining the catchment-scale degradation of a Himalayan wetland I. Rashid et al. 10.1016/j.envres.2023.115967
134. Impacts of climate change on water-related mosquito-borne diseases in temperate regions: A systematic review of literature and meta-analysis Z. Gizaw et al. 10.1016/j.actatropica.2024.107324
135. Mapping refugee populations at high resolution by unlocking humanitarian administrative data E. Darin et al. 10.1186/s41018-024-00157-6
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139. Spatial Population Distribution Data Disaggregation Based on SDGSAT-1 Nighttime Light and Land Use Data Using Guilin, China, as an Example C. Liu et al. 10.3390/rs15112926
140. EO4Migration: The Design of an EO-Based Solution in Support of Migrants’ Inclusion and Social-Cohesion Policies M. Aquilino et al. 10.3390/rs14174295
141. Towards an Improved Large-Scale Gridded Population Dataset: A Pan-European Study on the Integration of 3D Settlement Data into Population Modelling D. Palacios-Lopez et al. 10.3390/rs14020325
142. Assessing Spatiotemporal Changes of SDG Indicators at the Neighborhood Level in Guilin, China: A Geospatial Big Data Approach L. Han et al. 10.3390/rs14194985
143. Role of Chinese cities in abating aviation carbon emissions based on gridded population data and power law model F. Li et al. 10.1016/j.energy.2023.129762
144. La population vue du ciel : quand l’imagerie satellite vient au secours du recensement E. Darin et al. 10.3917/popu.2203.0467
145. Uncovering temporal changes in Europe’s population density patterns using a data fusion approach F. Batista e Silva et al. 10.1038/s41467-020-18344-5
146. Violent conflict exacerbated drought-related food insecurity between 2009 and 2019 in sub-Saharan Africa W. Anderson et al. 10.1038/s43016-021-00327-4
147. A review of climate security risk assessment tools B. Šedová et al. 10.1177/27538796241226996
148. Satellite-Based Human Settlement Datasets Inadequately Detect Refugee Settlements: A Critical Assessment at Thirty Refugee Settlements in Uganda J. Van Den Hoek & H. Friedrich 10.3390/rs13183574
149. Need for an Integrated Deprived Area “Slum” Mapping System (IDEAMAPS) in Low- and Middle-Income Countries (LMICs) D. Thomson et al. 10.3390/socsci9050080
150. Downscaling population and urban land use for socio-economic scenarios in the UK M. Merkle et al. 10.1007/s10113-022-01963-7
151. High-resolution gridded estimates of population sociodemographics from the 2020 census in California N. Depsky et al. 10.1371/journal.pone.0270746
152. How accurate is a poverty map based on remote sensing data? An application to Malawi R. van der Weide et al. 10.1016/j.jdeveco.2024.103352
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154. Mapping Changing Population Distribution on the Qinghai–Tibet Plateau since 2000 with Multi-Temporal Remote Sensing and Point-of-Interest Data L. Li et al. 10.3390/rs12244059
155. Exploring methods for mapping seasonal population changes using mobile phone data D. Woods et al. 10.1057/s41599-022-01256-8
156. Mapping Multi-Temporal Population Distribution in China from 1985 to 2010 Using Landsat Images via Deep Learning H. Zhuang et al. 10.3390/rs13173533
157. A subnational reproductive, maternal, newborn, child, and adolescent health and development atlas of India C. Pezzulo et al. 10.1038/s41597-023-01961-2
158. Experimental study of population density using an optimized random forest model L. Li et al. 10.1007/s11442-024-2264-5
159. Open and Consistent Geospatial Data on Population Density, Built-Up and Settlements to Analyse Human Presence, Societal Impact and Sustainability: A Review of GHSL Applications D. Ehrlich et al. 10.3390/su13147851
160. Gridded reconstruction of the population in the traditional cultivated region of China from 1776 to 1953 X. Zhang et al. 10.1007/s11430-020-9866-2
161. Delimitation of rural and urban areas from spatial data in statistical grids: the province of Jaén (Spain) as a case study J. Sánchez-Martínez & A. Garrido-Almonacid 10.1080/17445647.2024.2301980
162. Dynamics of population growth in secondary cities across southern Africa A. Zimmer et al. 10.1007/s10980-020-01086-6
163. Role of transportation network on population distribution evolution F. Zhao & H. Shang 10.1016/j.physa.2021.126076
164. A novel approach to improve population mapping considering facility-based service capacity and land livability Y. Ma et al. 10.1080/13658816.2024.2411434
165. Mapping population on Tibetan Plateau by fusing VIIRS data and nighttime Tencent location-based services data X. Ma et al. 10.1016/j.ecolind.2022.108893
166. Which Gridded Population Data Product Is Better? Evidences from Mainland Southeast Asia (MSEA) X. Yin et al. 10.3390/ijgi10100681
167. Towards a new spatial representation of faecal sources and pathways in unsewered urban catchments using open-source data M. Sultana et al. 10.2166/washdev.2023.227
168. A Novel Framework for Mapping Updated Fine-resolution Populations with Remote Sensing and Mobile Phone Data S. Wang & L. Wang 10.34133/remotesensing.0227
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170. Mortality Attributable to Ambient Air Pollution: A Review of Global Estimates A. Pozzer et al. 10.1029/2022GH000711
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173. High-resolution gridded population datasets for Latin America and the Caribbean using official statistics T. McKeen et al. 10.1038/s41597-023-02305-w
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176. Sustainability Assessment of Urban Public Transport for SDG Using Geospatial Big Data Q. Zhang et al. 10.3390/su16114542
177. Fine-Scale Population Estimation Based on Building Classifications: A Case Study in Wuhan S. Wang et al. 10.3390/fi13100251
178. Improving the accuracy of gridded population estimates in cities and slums to monitor SDG 11: Evidence from a simulation study in Namibia D. Thomson et al. 10.1016/j.landusepol.2022.106392
179. Data-Driven Insights into Population Exposure Risks: Towards Sustainable and Safe Urban Airspace Utilization by Unmanned Aerial Systems H. He et al. 10.3390/su151612247
180. A 100 m gridded population dataset of China's seventh census using ensemble learning and big geospatial data Y. Chen et al. 10.5194/essd-16-3705-2024
181. Estimation of flood-exposed population in data-scarce regions combining satellite imagery and high resolution hydrological-hydraulic modelling: A case study in the Licungo basin (Mozambique) L. Cea et al. 10.1016/j.ejrh.2022.101247
182. Population cluster data to assess the urban-rural split and electrification in Sub-Saharan Africa B. Khavari et al. 10.1038/s41597-021-00897-9
183. A review of geospatial methods for population estimation and their use in constructing reproductive, maternal, newborn, child and adolescent health service indicators K. Nilsen et al. 10.1186/s12913-021-06370-y
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