This Innovative Technology Experiences for Students and Teachers (ITEST) project has developed, implemented, and evaluated a series of innovative Socio-Environmental Science Investigations (SESI) using a geospatial curriculum approach. It is targeted for economically disadvantaged 9th grade high school students in Allentown, PA, and involves hands-on geospatial technology to help develop STEM-related skills. SESI focuses on societal issues related to environmental science. These issues are multi-disciplinary, involve decision-making that is based on the analysis of merged scientific and sociological data, and have direct implications for the social agency and equity milieu faced by these and other school students. This project employed a design partnership between Lehigh University natural science, social science, and education professors, high school science and social studies teachers, and STEM professionals in the local community to develop geospatial investigations with Web-based Geographic Information Systems (GIS). These were designed to provide students with geospatial skills, career awareness, and motivation to pursue appropriate education pathways for STEM-related occupations, in addition to building a more geographically and scientifically literate citizenry. The learning activities provide opportunities for students to collaborate, seek evidence, problem-solve, master technology, develop geospatial thinking and reasoning skills, and practice communication skills that are essential for the STEM workplace and beyond. Despite the accelerating growth in geospatial industries and congruence across STEM, few school-based programs integrate geospatial technology within their curricula, and even fewer are designed to promote interest and aspiration in the STEM-related occupations that will maintain American prominence in science and technology. The SESI project is based on a transformative curriculum approach for geospatial learning using Web GIS to develop STEM-related skills and promote STEM-related career interest in students who are traditionally underrepresented in STEM-related fields. This project attends to a significant challenge in STEM education: the recognized deficiency in quality locally-based and relevant high school curriculum for under-represented students that focuses on local social issues related to the environment. Environmental issues have great societal relevance, and because many environmental problems have a disproportionate impact on underrepresented and disadvantaged groups, they provide a compelling subject of study for students from these groups in developing STEM-related skills. Once piloted in the relatively challenging environment of an urban school with many unengaged learners, the results will be readily transferable to any school district to enhance geospatial reasoning skills nationally.

THE ADVANCED ENVIRONMENTAL SCIENCE AND BIOLOGY GEOINQUIRY COLLECTIONhttp://www.esri.com/geoinquiriesTo support Esri’s involvement in the White House ConnectED Initiative, GeoInquiry instructional materials using ArcGIS Online for high school biology education are now freely available.The Advanced Environmental Science and Biology GeoInquiry collection contains 15 free, web-mapping activities that correspond and extend map-based concepts in leading elementary textbooks. The activities use a standard inquiry-based instructional model, require only 15 minutes for a teacher to deliver, and are device/laptop agnostic. The activities harmonize with the Next Generation Science Standards. Activity topics include:• Population dynamics • Megacities • Down to the last drop • Dead zones (water pollution) • The Beagle’s Path • Primary productivity • Tropical Deforestation • Marine debris • El Nino (and climate) • Slowing malaria • Altered biomes • Spinning up wind power • Resource consumption and wealthTeachers, GeoMentors, and administrators can learn more at http://www.esri.com/geoinquiries

The air quality in Beijing, especially its PM2.5 level, has become of increasing public concern because of its importance and sensitivity related to health risks. A set of monitored PM2.5 data from 31 stations, released for the first time by the Beijing Environmental Protection Bureau, covering 37 days during autumn 2012, was processed using spatial interpolation and overlay analysis. Following analyses of these data, a distribution map of cumulative exceedance days of PM2.5 and a temporal variation map of PM2.5 for Beijing have been drawn. Computational and analytical results show periodic and directional trends of PM2.5 spreading and congregating in space, which reveals the regulation of PM2.5 overexposure on a discontinuous medium-term scale. With regard to the cumulative effect of PM2.5 on the human body, the harm from lower intensity overexposure in the medium term, and higher overexposure in the short term, are both obvious. Therefore, data of population distribution were integrated into the aforementioned PM2.5 spatial spectrum map. A spatial statistical analysis revealed the patterns of PM2.5 gross exposure and exposure probability of residents in the Beijing urban area. The methods and conclusions of this research reveal relationships between long-term overexposure to PM2.5 and people living in high-exposure areas of Beijing, during the autumn of 2012.

Dataset for the textbook Computational Methods and GIS Applications in Social Science (3rd Edition), 2023 Fahui Wang, Lingbo Liu Main Book Citation: Wang, F., & Liu, L. (2023). Computational Methods and GIS Applications in Social Science (3rd ed.). CRC Press. https://doi.org/10.1201/9781003292302 KNIME Lab Manual Citation: Liu, L., & Wang, F. (2023). Computational Methods and GIS Applications in Social Science - KNIME Lab Manual (1st ed.). CRC Press. https://doi.org/10.1201/9781003304357 Update Log the dataset and tool for ABM Crime Simulation were updated on August 3, 2023, the toolkits in CMGIS-V3-Tools was updated on August 3rd,2023. Report Issues on GitHub https://github.com/UrbanGISer/Computational-Methods-and-GIS-Applications-in-Social-Science Following the website of Fahui Wang : http://faculty.lsu.edu/fahui Contents Chapter 1. Getting Started with ArcGIS: Data Management and Basic Spatial Analysis Tools Case Study 1: Mapping and Analyzing Population Density Pattern in Baton Rouge, Louisiana Chapter 2. Measuring Distance and Travel Time and Analyzing Distance Decay Behavior Case Study 2A: Estimating Drive Time and Transit Time in Baton Rouge, Louisiana Case Study 2B: Analyzing Distance Decay Behavior for Hospitalization in Florida Chapter 3. Spatial Smoothing and Spatial Interpolation Case Study 3A: Mapping Place Names in Guangxi, China Case Study 3B: Area-Based Interpolations of Population in Baton Rouge, Louisiana Case Study 3C: Detecting Spatiotemporal Crime Hotspots in Baton Rouge, Louisiana Chapter 4. Delineating Functional Regions and Applications in Health Geography Case Study 4A: Defining Service Areas of Acute Hospitals in Baton Rouge, Louisiana Case Study 4B: Automated Delineation of Hospital Service Areas in Florida Chapter 5. GIS-Based Measures of Spatial Accessibility and Application in Examining Healthcare Disparity Case Study 5: Measuring Accessibility of Primary Care Physicians in Baton Rouge Chapter 6. Function Fittings by Regressions and Application in Analyzing Urban Density Patterns Case Study 6: Analyzing Population Density Patterns in Chicago Urban Area >Chapter 7. Principal Components, Factor and Cluster Analyses and Application in Social Area Analysis Case Study 7: Social Area Analysis in Beijing Chapter 8. Spatial Statistics and Applications in Cultural and Crime Geography Case Study 8A: Spatial Distribution and Clusters of Place Names in Yunnan, China Case Study 8B: Detecting Colocation Between Crime Incidents and Facilities Case Study 8C: Spatial Cluster and Regression Analyses of Homicide Patterns in Chicago Chapter 9. Regionalization Methods and Application in Analysis of Cancer Data Case Study 9: Constructing Geographical Areas for Mapping Cancer Rates in Louisiana Chapter 10. System of Linear Equations and Application of Garin-Lowry in Simulating Urban Population and Employment Patterns Case Study 10: Simulating Population and Service Employment Distributions in a Hypothetical City Chapter 11. Linear and Quadratic Programming and Applications in Examining Wasteful Commuting and Allocating Healthcare Providers Case Study 11A: Measuring Wasteful Commuting in Columbus, Ohio Case Study 11B: Location-Allocation Analysis of Hospitals in Rural China Chapter 12. Monte Carlo Method and Applications in Urban Population and Traffic Simulations Case Study 12A. Examining Zonal Effect on Urban Population Density Functions in Chicago by Monte Carlo Simulation Case Study 12B: Monte Carlo-Based Traffic Simulation in Baton Rouge, Louisiana Chapter 13. Agent-Based Model and Application in Crime Simulation Case Study 13: Agent-Based Crime Simulation in Baton Rouge, Louisiana Chapter 14. Spatiotemporal Big Data Analytics and Application in Urban Studies Case Study 14A: Exploring Taxi Trajectory in ArcGIS Case Study 14B: Identifying High Traffic Corridors and Destinations in Shanghai Dataset File Structure 1 BatonRouge Census.gdb BR.gdb 2A BatonRouge BR_Road.gdb Hosp_Address.csv TransitNetworkTemplate.xml BR_GTFS Google API Pro.tbx 2B Florida FL_HSA.gdb R_ArcGIS_Tools.tbx (RegressionR) 3A China_GX GX.gdb 3B BatonRouge BR.gdb 3C BatonRouge BRcrime R_ArcGIS_Tools.tbx (STKDE) 4A BatonRouge BRRoad.gdb 4B Florida FL_HSA.gdb HSA Delineation Pro.tbx Huff Model Pro.tbx FLplgnAdjAppend.csv 5 BRMSA BRMSA.gdb Accessibility Pro.tbx 6 Chicago ChiUrArea.gdb R_ArcGIS_Tools.tbx (RegressionR) 7 Beijing BJSA.gdb bjattr.csv R_ArcGIS_Tools.tbx (PCAandFA, BasicClustering) 8A Yunnan YN.gdb R_ArcGIS_Tools.tbx (SaTScanR) 8B Jiangsu JS.gdb 8C Chicago ChiCity.gdb cityattr.csv R_ArcGIS_Tools.tbx (PCAandFA, SpatialRegressionModel) 9 Louisiana Louisiana.gdb MLR Tools Pro.tbx R_ArcGIS_Tools.tbx (RegionalizationR) 10 SimuCity SimuCity.gdb Garin-Lowry.tbx 11A Columbus Columbus.gdb R_ArcGIS_Tools.tbx (WasteCommuteR) 11B Xiantao XT.gdb R_ArcGIS_Tools.tbx (MiniMaxR, MAEP) 12A Chicago ZoneEffect.gdb 12B BRMSA BRMSAmc.gdb MCSimulation.tbx 13 ABMSIM Data ...

Data for maps and figures in "Global Potential for Harvesting Drinking Water from Air using Solar Energy" in Nature.

THE ADVANCED ENVIRONMENTAL SCIENCE AND BIOLOGY GEOINQUIRY COLLECTIONhttp://www.esri.com/geoinquiriesTo support Esri’s involvement in the White House ConnectED Initiative, GeoInquiry instructional materials using ArcGIS Online for high school biology education are now freely available.The Advanced Environmental Science and Biology GeoInquiry collection contains 15 free, web-mapping activities that correspond and extend map-based concepts in leading elementary textbooks. The activities use a standard inquiry-based instructional model, require only 15 minutes for a teacher to deliver, and are device/laptop agnostic. The activities harmonize with the Next Generation Science Standards. Activity topics include:• Population dynamics • Megacities • Down to the last drop • Dead zones (water pollution) • The Beagle’s Path • Primary productivity • Tropical Deforestation • Marine debris • El Nino (and climate) • Slowing malaria • Altered biomes • Spinning up wind power • Resource consumption and wealthTeachers, GeoMentors, and administrators can learn more at http://www.esri.com/geoinquiries

GIS dataset with TIFFs and tsw files

Environmental pollution is a persistent problem in terrestrial ecosystems, including remote mountain areas. This study investigates the extent and patterns of littering on three popular hiking trails among mountaineers and tourists in the Dolomites range located in northeastern Italy. The data was collected adopting a citizen science approach with the participation of university students surveying the trails and recording the macroscopic waste items through a GPS-based offline platform. The waste items were categorized according to their material type, usage, and geographical location, and the sorted data was applied to Esri GIS ArcMapTM 10.8.1.

Explore water pollution and its impact on marine life.THE ADVANCED ENVIRONMENTAL SCIENCE AND BIOLOGY GEOINQUIRY COLLECTIONhttp://www.esri.com/geoinquiriesTo support Esri’s involvement in the White House ConnectED Initiative, GeoInquiry instructional materials using ArcGIS Online for high school biology education are now freely available.The Advanced Environmental Science and Biology GeoInquiry collection contains 15 free, web-mapping activities that correspond and extend map-based concepts in leading elementary textbooks. The activities use a standard inquiry-based instructional model, require only 15 minutes for a teacher to deliver, and are device/laptop agnostic. The activities harmonize with the Next Generation Science Standards. Teachers, GeoMentors, and administrators can learn more at http://www.esri.com/geoinquiries

GIS Layers used to create the hunting habitat model, which include Cattle density, Distance from edge, Dominant landcover, Forest edge density, Forest patch size, Improved pasture patch size, Landcover, and Percent forest cover. Area of analysis defined by Minimum Convex Polygons created from Florida panther GPS data.

This activity highlights patterns of poverty and wealth to address overconsumption and resource extraction.THE ADVANCED ENVIRONMENTAL SCIENCE AND BIOLOGY GEOINQUIRY COLLECTIONhttp://www.esri.com/geoinquiriesTo support Esri’s involvement in the White House ConnectED Initiative, GeoInquiry instructional materials using ArcGIS Online for high school biology education are now freely available.The Advanced Environmental Science and Biology GeoInquiry collection contains 15 free, web-mapping activities that correspond and extend map-based concepts in leading elementary textbooks. The activities use a standard inquiry-based instructional model, require only 15 minutes for a teacher to deliver, and are device/laptop agnostic. The activities harmonize with the Next Generation Science Standards. Activity topics include:• Population dynamics • Megacities • Down to the last drop • Dead zones (water pollution) • The Beagle’s Path • Primary productivity • Tropical Deforestation • Marine debris • El Nino (and climate) • Slowing malaria • Altered biomes • Spinning up wind power • Resource consumption and wealthTeachers, GeoMentors, and administrators can learn more at http://www.esri.com/geoinquiries

This fileset contains geo-referenced data files. Latitude and longitude are WGS-84 unless described otherwise.

Shapefile used in the various maps in the study. Visit https://dataone.org/datasets/sha256%3A2fdaa83821076dc77d906d53f13fd8aaa6ecb2f8bf1e16082352037b5459f465 for complete metadata about this dataset.

1 =  Tree cover; 2 =  Shrub cover; 3 =  Herbaceous vegetation/Grassland; 4 =  Cultivated and managed; 5 =  Mosaic of cultivated and managed/natural vegetation.

Introduction​ Promoting well-being is one of the key targets of Sustainable Development Goals at the United Nations. Many governments worldwide are incorporating subjective well-being (SWB) indicators to complement traditional objective and economic metrics. Our Twitter Sentiment Geographical Index (TSGI) can provide a high granularity monitor of well-being worldwide. This dataset is a joint effort of the Sustainable Urbanization Lab at MIT and Center for Geographic Analysis at Harvard. ===== ===== ===== ===== ===== ===== ===== ===== ===== ===== ===== ===== Granularity Geographical granularity: We provide a sentiment index on four levels: Globe, Country, State/Province, County/City Temporal granularity: The data covers 2012 to the present. And we update the sentiment data on a monthly basis. ===== ===== ===== ===== ===== ===== ===== ===== ===== ===== ===== ===== Fields DATE---- date ---- the date of the sentiment index NAME_0 ---- string ---- the country name NAME_1 ---- string ---- the state/province name NAME_2 ---- string ---- the county/city name SCORE ---- float ---- a float value between 0 and 1 representing the sentiment index where 1 represents a positive sentiment and 0 represents the negative sentiment. N ---- int ---- the number of posts generated given the specific date ===== ===== ===== ===== ===== ===== ===== ===== ===== ===== ===== ===== Citation rule If you use the TSGI in your research, please cite it as below: "Twitter Sentiment Geographical Index (https://doi.org/10.7910/DVN/3IL00Q)" ===== ===== ===== ===== ===== ===== ===== ===== ===== ===== ===== ===== Additional information For more information regarding the source dataset, please visit: here This dataset is free of usage for academic purposes. Please contact us should you have any questions or other usage cases. Thanks!

This folder contains the hydrological and geological data from DK-model2019. Grid files and .shp files are assembled in ArcGIS Pro v.3.0.1. DK-model2019 setup and calibration is described in GEUS report 2019/31.

Funded by a grant from the Sloan Foundation, and with support from Massachusetts Open Cloud, the Center for Geographic Analysis(CGA) at Harvard developed a “big geodata”, remotely hosted, real-time-updated dataset which is a prototype for a new data type hosted outside Dataverse which supports streaming updates, and is accessed via an API. The CGA developed 1) the software and hardware platform to support interactive exploration of a billion spatio-temporal objects, nicknamed the "BOP" (billion object platform) 2) an API to provide query access to the archive from Dataverse 3) client-side tools for querying/visualizing the contents of the archive and extracting data subsets. This project is currently no longer active. For more information please see: http://gis.harvard.edu/services/project-consultation/project-resume/billion-object-platform-bop. “Geotweets” are tweets containing a GPS coordinate from the originating device. Currently 1-2% of tweets are geotweets, about 8 million per day. The CGA has been harvesting geotweets since 2012.

This folder contains contain various results simulated with DK-model2019. Grid files and .shp files are assembled in ArcGIS Pro v.3.0.1. For results regarding streamflow, seepage from groundwater to streams, depth to groundwater table, net precipitation, recharge and water velocity, refer to GEUS report 2019/32. For results regarding delineation of groundwater bodies, contact between groundwater bodies and surface water, abstractions linked to the groundwater bodies, refer to GEUS report 2020/1. For results, regarding changes in simulated groundwater head levels, change in depth to the groundwater table and change in stream runoff, refer again to GEUS report 2019/32. For a detailed description of the procedure, including the distribution of extracted amounts per water plant, connection to groundwater, etc., refer to GEUS report 2021/19 Annex 6.

A platform-agnostic and living geographic information data dictionary for trafficking of wild flora and fauna based on diverse stakeholder input and with the potential to accelerate convergence of information and increase efficacy of interventions.

The aerial photographs, taken on the 6th of February 1975 at a scale 1: 50 000, were obtained from the Survey of Kenya and were used to generate my original data.