163 citations as recorded by crossref.

1. The utility of built environment geospatial data for high-resolution dasymetric global population modeling S. Rubinyi et al. 10.1016/j.compenvurbsys.2021.101594
2. The Role of Global Data Sets for Riverine Flood Risk Management at National Scales M. Bernhofen et al. 10.1029/2021WR031555
3. 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
4. Spatial aggregation with respect to a population distribution: Impact on inference J. Paige et al. 10.1016/j.spasta.2022.100714
5. Combining Conventional Statistics and Big Data to Map Global Tourism Destinations Before COVID-19 C. Adamiak & B. Szyda 10.1177/00472875211051418
6. 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
7. 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
8. Estimating population and urban areas at risk of coastal hazards, 1990–2015: how data choices matter K. MacManus et al. 10.5194/essd-13-5747-2021
9. Monitoring and projecting global hunger: Are we on track? M. Cooper et al. 10.1016/j.gfs.2021.100568
10. 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
11. Intercensal Updating Using Structure-Preserving Methods and Satellite Imagery T. Koebe et al. 10.1111/rssa.12802
12. Global maps of 3D built-up patterns for urban morphological analysis M. Li et al. 10.1016/j.jag.2022.103048
13. Assessing OSM building completeness using population data Y. Zhang et al. 10.1080/13658816.2021.2023158
14. Accounting for internal migration in spatial population projections—a gravity-based modeling approach using the Shared Socioeconomic Pathways L. Reimann et al. 10.1088/1748-9326/ac0b66
15. Estimating Chinese residential populations from analysis of impervious surfaces derived from satellite images S. Wei et al. 10.1080/01431161.2020.1841322
16. Differences between gridded population data impact measures of geographic access to healthcare in sub-Saharan Africa F. Hierink et al. 10.1038/s43856-022-00179-4
17. Multi-Resolution Population Mapping Based on a Stepwise Downscaling Approach Using Multisource Data Y. Jin et al. 10.3390/rs15071947
18. An introduction to DUIA: The database on urban inequality and amenities F. Ramos et al. 10.1371/journal.pone.0253824
19. Improving the accuracy of extant gridded population maps using multisource map fusion P. Gao et al. 10.1080/15481603.2021.2012371
20. Machine learning for spatial analyses in urban areas: a scoping review Y. Casali et al. 10.1016/j.scs.2022.104050
21. Mapping Deprived Urban Areas Using Open Geospatial Data and Machine Learning in Africa M. Owusu et al. 10.3390/urbansci7040116
22. A review of freely accessible global datasets for the study of floods, droughts and their interactions with human societies S. Lindersson et al. 10.1002/wat2.1424
23. The impact of flooding on food security across Africa C. Reed et al. 10.1073/pnas.2119399119
24. U-Net-Id, an Instance Segmentation Model for Building Extraction from Satellite Images—Case Study in the Joanópolis City, Brazil F. Wagner et al. 10.3390/rs12101544
25. Increasing Spatial Mismatch of Cropland-Grain Production-Population in China over the Past Two Decades L. Li et al. 10.3390/land11101685
26. Improving access to care and community health in Haiti with optimized community health worker placement C. Champagne et al. 10.1371/journal.pgph.0000167
27. 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
28. Social vulnerability of the people exposed to wildfires in U.S. West Coast states A. Modaresi Rad et al. 10.1126/sciadv.adh4615
29. 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
30. Spatially Explicit Mapping of Historical Population Density with Random Forest Regression: A Case Study of Gansu Province, China, in 1820 and 2000 F. Wang et al. 10.3390/su12031231
31. 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
32. A 100 m population grid in the CONUS by disaggregating census data with open-source Microsoft building footprints X. Huang et al. 10.1080/20964471.2020.1776200
33. Flood hazard potential reveals global floodplain settlement patterns L. Devitt et al. 10.1038/s41467-023-38297-9
34. 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
35. Predicting Near-Future Built-Settlement Expansion Using Relative Changes in Small Area Populations J. Nieves et al. 10.3390/rs12101545
36. How much time have we got? K. Kausrud et al. 10.3897/aca.4.e68934
37. Occupations on the map: Using a super learner algorithm to downscale labor statistics M. van Dijk et al. 10.1371/journal.pone.0278120
38. Applying a zero-corrected, gravity model estimator reduces bias due to heterogeneity in healthcare utilization in community-scale, passive surveillance datasets of endemic diseases M. Evans et al. 10.1038/s41598-023-48390-0
39. Small area population denominators for improved disease surveillance and response A. Tatem 10.1016/j.epidem.2022.100597
40. 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
41. Human populations in the world’s mountains: Spatio-temporal patterns and potential controls J. Thornton et al. 10.1371/journal.pone.0271466
42. 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
43. 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
44. 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
45. 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
46. First year with COVID-19: Assessment and prospects R. Bergquist et al. 10.4081/gh.2020.953
47. Modeling climate migration: dead ends and new avenues R. Beyer et al. 10.3389/fclim.2023.1212649
48. 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
49. Recommendations to improve the interpretation of global flood forecasts to support international humanitarian operations for tropical cyclones L. Speight et al. 10.1111/jfr3.12952
50. Satellite imaging reveals increased proportion of population exposed to floods B. Tellman et al. 10.1038/s41586-021-03695-w
51. 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
52. EO4Migration: The Design of an EO-Based Solution in Support of Migrants’ Inclusion and Social-Cohesion Policies M. Aquilino et al. 10.3390/rs14174295
53. 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
54. 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
55. 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
56. 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
57. La population vue du ciel : quand l’imagerie satellite vient au secours du recensement E. Darin et al. 10.3917/popu.2203.0467
58. 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
59. 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
60. Fine-grained, spatiotemporal datasets measuring 200 years of land development in the United States J. Uhl et al. 10.5194/essd-13-119-2021
61. 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
62. Disaggregating Population Data and Evaluating the Accuracy of Modeled High-Resolution Population Distribution—The Case Study of Germany S. Eichhorn 10.3390/su12103976
63. Need for an Integrated Deprived Area “Slum” Mapping System (IDEAMAPS) in Low- and Middle-Income Countries (LMICs) D. Thomson et al. 10.3390/socsci9050080
64. Looking Back, Looking Forward: Progress and Prospect for Spatial Demography S. Matthews et al. 10.1007/s40980-021-00084-9
65. 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
66. 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
67. Disaggregating population data for assessing progress of SDGs: methods and applications Y. Qiu et al. 10.1080/17538947.2021.2013553
68. Assessing the relative accuracy of gridded population sampling: results from an election survey experiment S. Ford & M. Kirwin 10.1080/13645579.2022.2091200
69. Downscaling population and urban land use for socio-economic scenarios in the UK M. Merkle et al. 10.1007/s10113-022-01963-7
70. High-resolution gridded estimates of population sociodemographics from the 2020 census in California N. Depsky et al. 10.1371/journal.pone.0270746
71. GHS-POP Accuracy Assessment: Poland and Portugal Case Study B. Calka & E. Bielecka 10.3390/rs12071105
72. 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
73. Gridded population survey sampling: a systematic scoping review of the field and strategic research agenda D. Thomson et al. 10.1186/s12942-020-00230-4
74. High-resolution population estimation using household survey data and building footprints G. Boo et al. 10.1038/s41467-022-29094-x
75. 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
76. Exploring methods for mapping seasonal population changes using mobile phone data D. Woods et al. 10.1057/s41599-022-01256-8
77. Mapping Multi-Temporal Population Distribution in China from 1985 to 2010 Using Landsat Images via Deep Learning H. Zhuang et al. 10.3390/rs13173533
78. The Missing Millions in Maps: Exploring Causes of Uncertainties in Global Gridded Population Datasets M. Kuffer et al. 10.3390/ijgi11070403
79. 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
80. A subnational reproductive, maternal, newborn, child, and adolescent health and development atlas of India C. Pezzulo et al. 10.1038/s41597-023-01961-2
81. 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
82. Improvement of a Dasymetric Method for Implementing Sustainable Development Goal 11 Indicators at an Intra-Urban Scale M. Aquilino et al. 10.3390/rs13142835
83. 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
84. Different Spatiotemporal Patterns in Global Human Population and Built‐Up Land J. Gao & B. O'Neill 10.1029/2020EF001920
85. 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
86. Dynamics of population growth in secondary cities across southern Africa A. Zimmer et al. 10.1007/s10980-020-01086-6
87. Multiple Global Population Datasets: Differences and Spatial Distribution Characteristics R. Chen et al. 10.3390/ijgi9110637
88. Role of transportation network on population distribution evolution F. Zhao & H. Shang 10.1016/j.physa.2021.126076
89. Asset exposure data for global physical risk assessment S. Eberenz et al. 10.5194/essd-12-817-2020
90. Spatiotemporal dynamics of population density in China using nighttime light and geographic weighted regression method* W. Guo et al. 10.1080/17538947.2023.2233493
91. Incorporating Geographic Information Science and Technology in Response to the COVID-19 Pandemic C. Smith & J. Mennis 10.5888/pcd17.200246
92. Evaluating spatial statistical and machine learning models in urban dynamic population mapping Z. Cheng et al. 10.1177/27541231221114169
93. 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
94. Which Gridded Population Data Product Is Better? Evidences from Mainland Southeast Asia (MSEA) X. Yin et al. 10.3390/ijgi10100681
95. 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
96. 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
97. Population development as a driver of coastal risk: Current trends and future pathways L. Reimann et al. 10.1017/cft.2023.3
98. 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
99. Experimentelle georeferenzierte Bevölkerungszahl auf Basis der Bevölkerungsfortschreibung und Mobilfunkdaten S. Hadam 10.1007/s11943-023-00320-2
100. Mapping Gridded Gross Domestic Product Distribution of China Using Deep Learning With Multiple Geospatial Big Data Y. Chen et al. 10.1109/JSTARS.2022.3148448
101. Mortality Attributable to Ambient Air Pollution: A Review of Global Estimates A. Pozzer et al. 10.1029/2022GH000711
102. Mapping Human Activity Volumes Through Remote Sensing Imagery X. Xing et al. 10.1109/JSTARS.2020.3023730
103. Using satellite imagery to understand and promote sustainable development M. Burke et al. 10.1126/science.abe8628
104. 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
105. High-resolution gridded population datasets for Latin America and the Caribbean using official statistics T. McKeen et al. 10.1038/s41597-023-02305-w
106. Spatial access inequities and childhood immunisation uptake in Kenya N. Joseph et al. 10.1186/s12889-020-09486-8
107. Assessing OSM building completeness for almost 13,000 cities globally Q. Zhou et al. 10.1080/17538947.2022.2159550
108. Approaches and tools for user-driven provenance and data quality information in spatial data infrastructures J. Fischer et al. 10.1080/17538947.2023.2198778
109. Small area population denominators for improved disease surveillance and response A. Tatem 10.1016/j.epidem.2022.100641
110. Global riverine flood risk – how do hydrogeomorphic floodplain maps compare to flood hazard maps? S. Lindersson et al. 10.5194/nhess-21-2921-2021
111. Global Harmonization of Urbanization Measures: Proceed with Care D. Balk et al. 10.3390/rs13244973
112. 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
113. Fine-Scale Population Estimation Based on Building Classifications: A Case Study in Wuhan S. Wang et al. 10.3390/fi13100251
114. Climatic Criteria of the Need for Preventive Adaptation A. Ginzburg et al. 10.1134/S0001433822060068
115. 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
116. 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
117. 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
118. 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
119. Mapping monthly population distribution and variation at 1-km resolution across China Z. Cheng et al. 10.1080/13658816.2020.1854767
120. 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
121. 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
122. 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
123. A Global Analysis of the Relationship Between Urbanization and Fatalities in Earthquake-Prone Areas C. He et al. 10.1007/s13753-021-00385-z
124. Global trends and local variations in land take per person M. Li et al. 10.1016/j.landurbplan.2021.104308
125. 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
126. A grid-based sample design framework for household surveys G. Boo et al. 10.12688/gatesopenres.13107.1
127. 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
128. Combining Luojia1-01 Nighttime Light and Points-of-Interest Data for Fine Mapping of Population Spatialization Based on the Zonal Classification Method W. Guo et al. 10.1109/JSTARS.2023.3238188
129. Mapping the Urban Population in Residential Neighborhoods by Integrating Remote Sensing and Crowdsourcing Data C. Jing et al. 10.3390/rs12193235
130. The Role of Earth Observation in an Integrated Deprived Area Mapping “System” for Low-to-Middle Income Countries M. Kuffer et al. 10.3390/rs12060982
131. Fire Danger Observed from Space M. Pettinari & E. Chuvieco 10.1007/s10712-020-09610-8
132. Gridded population mapping for Germany based on building density, height and type from Earth Observation data using census disaggregation and bottom-up estimates F. Schug et al. 10.1371/journal.pone.0249044
133. Evaluating the Accuracy of Gridded Population Estimates in Slums: A Case Study in Nigeria and Kenya D. Thomson et al. 10.3390/urbansci5020048
134. Multi-Scale Estimation of Land Use Efficiency (SDG 11.3.1) across 25 Years Using Global Open and Free Data M. Schiavina et al. 10.3390/su11205674
135. Human and infrastructure exposure to large wildfires in the United States A. Modaresi Rad et al. 10.1038/s41893-023-01163-z
136. Evaluation of NLDAS-2 and Downscaled Air Temperature data in Florida J. Jung et al. 10.1080/02723646.2021.1928878
137. Empiric recommendations for population disaggregation under different data scenarios M. Sapena et al. 10.1371/journal.pone.0274504
138. 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
139. Measuring residential segregation in multi‐ethnic and unequal European cities F. Benassi et al. 10.1111/imig.13018
140. Understanding dynamics of population flood exposure in Canada with multiple high-resolution population datasets M. Mohanty & S. Simonovic 10.1016/j.scitotenv.2020.143559
141. Implications for Tracking SDG Indicator Metrics with Gridded Population Data C. Tuholske et al. 10.3390/su13137329
142. Population boom in the borderlands globally C. Xiao et al. 10.1016/j.jclepro.2022.133685
143. Human displacements from Tropical Cyclone Idai attributable to climate change B. Mester et al. 10.5194/nhess-23-3467-2023
144. The uncertainty of flood frequency analyses in hydrodynamic model simulations X. Zhou et al. 10.5194/nhess-21-1071-2021
145. Estimating local agricultural gross domestic product (AgGDP) across the world Y. Ru et al. 10.5194/essd-15-1357-2023
146. 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
147. The Unseen Population: Underestimation of Slum Dwellers in Cities of the Global South J. Breuer et al. 10.2139/ssrn.4098362
148. Mapping Impervious Surface Areas Using Time-Series Nighttime Light and MODIS Imagery Y. Tang et al. 10.3390/rs13101900
149. Global flood exposure from different sized rivers M. Bernhofen et al. 10.5194/nhess-21-2829-2021
150. Measuring the contribution of built-settlement data to global population mapping J. Nieves et al. 10.1016/j.ssaho.2020.100102
151. 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
152. Two centuries of settlement and urban development in the United States S. Leyk et al. 10.1126/sciadv.aba2937
153. 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
154. 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
155. Fine-scale population spatialization data of China in 2018 based on real location-based big data M. Chen et al. 10.1038/s41597-022-01740-5
156. Local Population Mapping Using a Random Forest Model Based on Remote and Social Sensing Data: A Case Study in Zhengzhou, China G. Qiu et al. 10.3390/rs12101618
157. Population spatialization with pixel-level attribute grading by considering scale mismatch issue in regression modeling Y. Mei et al. 10.1080/10095020.2021.2021785
158. Technology transfer for sustainable rural development: evidence from homestead withdrawal with compensation in Chengdu–Chongqing T. Xia et al. 10.1007/s10961-023-10019-9
159. 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
160. Unveiling the Potential of Machine Learning Applications in Urban Planning Challenges S. Koutra & C. Ioakimidis 10.3390/land12010083
161. Evaluating nighttime lights and population distribution as proxies for mapping anthropogenic CO2emission in Vietnam, Cambodia and Laos A. Gaughan et al. 10.1088/2515-7620/ab3d91
162. Development and implementation of automatic metadata generation framework for SDI using OSS: a case study of Indian NSDI S. Agrawal et al. 10.1007/s12517-022-09635-w
163. People and Pixels 20 years later: the current data landscape and research trends blending population and environmental data T. Kugler et al. 10.1007/s11111-019-00326-5