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.

Geographic Information Systems (GIS) technology allows users to make maps and analyze data. Savvy educators have been using GIS since the early 1990s, but online GIS makes it easy for educators to get started quickly, even just learning on their own, online. Here is a sequenced set of resources and activities with which to begin; they start fast and easy, scaffold ideas and skills, and generally take more time and require more background as one progresses, so items should be experienced in order.

Bolton & Menk, an engineering planning and consulting firm from the Midwestern United States has released a series of illustrated children’s books as a way of helping young people discover several different professions that typically do not get as much attention as other more traditional ones do.Topics of the award winning book series include landscape architecture, civil engineering, water resource engineering, urban planning and now Geographic Information Systems (GIS). The books are available free online in digital format, and easily accessed via a laptop, smart phone or tablet.The book Lindsey the GIS Specialist – A GIS Mapping Story Tyler Danielson, covers some the basics of what geographic information is and the type of work that a GIS Specialist does. It explains what the acronym GIS means, the different types of geospatial data, how we collect data, and what some of the maps a GIS Specialist creates would be used for.Click here to check out the GIS Specialist – A GIS Mapping Story e-book

Tagged image tiles as well as the Faster-RCNN framework for automatic extraction of road intersection points from USGS historical maps of the United States of America. The data and code have been prepared for the paper entitled "Automatic extraction of road intersection points from USGS historical map series using deep convolutional neural networks" submitted to "International Journal of Geographic Information Science". The image tiles have been tagged manually. The Faster RCNN framework (see https://arxiv.org/abs/1611.10012) was captured from:https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/detection_model_zoo.md

Count of students in each grade (PK-12) enrolled in each Alaska public school. These data are taken from the official October 1 student count. This data set features historical data from the 2012-2013 school year to the present. Source: Alaska Department of Education & Early Development

This data has been visualized in a Geographic Information Systems (GIS) format and is provided as a service in the DCRA Information Portal by the Alaska Department of Commerce, Community, and Economic Development Division of Community and Regional Affairs (SOA DCCED DCRA), Research and Analysis section. SOA DCCED DCRA Research and Analysis is not the authoritative source for this data. For more information and for questions about this data, see: Alaska Department of Education & Early Development Data Center.

This dataset provides annual nomenclatures and shapefiles of academies of metropolitan France from 1870 to 1940. It is part of the TRF-GIS Dataverse.

The Geographic Information System (GIS) Analytics market is experiencing robust growth, projected to reach $15.10 billion in 2025 and maintain a Compound Annual Growth Rate (CAGR) of 12.41% from 2025 to 2033. This expansion is fueled by several key drivers. Increasing adoption of cloud-based GIS solutions enhances accessibility and scalability for diverse industries. The growing need for data-driven decision-making across sectors like retail, real estate, government, and telecommunications is a significant catalyst. Furthermore, advancements in artificial intelligence (AI) and machine learning (ML) integrated with GIS analytics are revolutionizing spatial data analysis, enabling more sophisticated predictive modeling and insightful interpretations. The market's segmentation reflects this broad adoption, with retail and real estate, government and utilities, and telecommunications representing key end-user segments, each leveraging GIS analytics for distinct applications such as location optimization, infrastructure management, and network planning. Competitive pressures are shaping the market landscape, with established players like Esri, Trimble, and Autodesk innovating alongside emerging tech companies focusing on AI and specialized solutions. The North American market currently holds a significant share, driven by early adoption and technological advancements. However, Asia-Pacific is expected to witness substantial growth due to rapid urbanization and increasing investment in infrastructure projects. Market restraints primarily involve the high cost of implementation and maintenance of advanced GIS analytics solutions and the need for skilled professionals to effectively utilize these technologies. However, the overall outlook remains extremely positive, driven by continuous technological innovation and escalating demand across multiple sectors. The future trajectory of the GIS analytics market hinges on several factors. Continued investment in research and development, especially in AI and ML integration, will be crucial for unlocking new possibilities. Furthermore, the simplification of GIS analytics software and the development of user-friendly interfaces will broaden accessibility beyond specialized technical experts. Growing data volumes from various sources (IoT, remote sensing) present both opportunities and challenges; efficient data management and analytics techniques will be paramount. The market's success also depends on addressing cybersecurity concerns related to sensitive geospatial data. Strong partnerships between technology providers and end-users will be vital in optimizing solution implementation and maximizing return on investment. Government initiatives promoting the use of GIS technology for smart city development and infrastructure planning will also play a significant role in market expansion. Overall, the GIS analytics market is poised for sustained growth, driven by technological advancements, increasing data availability, and heightened demand for location-based intelligence across a wide range of industries.

This file provides the raw data of an online survey intended at gathering information regarding remote sensing (RS) and Geographical Information Systems (GIS) for conservation in academic education. The aim was to unfold best practices as well as gaps in teaching methods of remote sensing/GIS, and to help inform how these may be adapted and improved. A total of 73 people answered the survey, which was distributed through closed mailing lists of universities and conservation groups.

This feature class (shapefile) consists of point locations of public, private, and charter schools including pre-schools, day care facilities, adult and vocational schools in New Jersey, with minimal attributes. Most of the public schools were initially located in 2003 by the New Jersey Department of Environmental Protection, and were checked in 2007-2016 by the NJ Office of Geographic Information Systems (OGIS), other organizations and volunteers. Charter schools were located in 2011 and checked through 2016 by OGIS against the 2016 NJ Department of Education table of public schools that also lists charter schools.Private schools were located initially in 2010, and updated later in 2014 only for Somerset County using the spatial data provided by Somerset County GIS team. The present data set is the result of checking and updating the previous locations by processing the tabular data that were acquired from NJDOE in August, 2016.

© Most of the public school records were derived from 2003 data sets created by the New Jersey Department of Environmental Protection. Special acknowledgements are to people of the following organizations who made a significant contribution to school location verification process: Seth Hackman (NJDEP), Salem county GIS; Morris County GIS; Atlantic County GIS; Cape May County GIS; DVRPC; Hunterdon County GIS; Somerset County GIS; Warren County Prosecutor's Office; Westfield Engineering; WFS (for Mercer, Middlesex, Burlington and Camden Co.) ; Monmouth GIS; voluneers and state employees who made site visits on their own time: Charles Colvard, Dominic Juliano, Matt Lawson, Richard Rabinowitz, Amy J. Ferdinand, Rebecca French-Mesch.

This layer is a component of Sites and Facilities.

Count of high school graduates for each public school in Alaska. Data covers the School Year 2013 to the present. Each year's count includes students graduating at any point during the school year (July 1 to June 30).Source: Alaska Department of Education & Early Development

This data has been visualized in a Geographic Information Systems (GIS) format and is provided as a service in the DCRA Information Portal by the Alaska Department of Commerce, Community, and Economic Development Division of Community and Regional Affairs (SOA DCCED DCRA), Research and Analysis section. SOA DCCED DCRA Research and Analysis is not the authoritative source for this data. For more information and for questions about this data, see: Alaska Department of Education & Early Development Data Center.

According to many previous studies, application of remote sensing for the complex and heterogeneous urban environments in Sub-Saharan African countries is challenging due to the spectral confusion among features caused by diversity of construction materials. Resorting to classification based on spectral indices that are expected to better highlight features of interest and to be prone to unsupervised classification, this study aims (1) to evaluate the effectiveness of index-based classification for Land Use Land Cover (LULC) using an unsupervised machine learning algorithm Product Quantized K-means (PQk-means); and (2) to monitor the urban expansion of Luanda, the capital city of Angola in a Logistic Regression Model (LRM). Comparison with state-of-the-art algorithms shows that unsupervised classification by means of spectral indices is effective for the study area and can be used for further studies. The built-up area of Luanda has increased from 94.5 km2 in 2000 to 198.3 km2 in 2008 and to 468.4 km2 in 2018, mainly driven by the proximity to the already established residential areas and to the main roads as confirmed by the logistic regression analysis. The generated probability maps show high probability of urban growth in the areas where government had defined housing programs.

Abstract

The Master List of Schools is a record of all schools in South Africa. The data forms part of the national Education Management Information Systems (EMIS) database used to inform education policymakers and managers in the Department of Basic Education (DBE) and the Provincial education departments, as well as to provide valuable information to external stakeholders. The list is maintained by provincial departments and regularly sent to DBE for updating. A key function of the master list is to uniquely identify each school in the country through a school identifier called the EMIS number. Additionally, the list contains data on school quintiles - categories (quintiles) based on the socioeconomic status of the community in which the school is situated. Analyses comparing schools' performance often use school quintiles as control measures for socioeconomic status, to take into account the effect of, for example, poor infrastructure, shortage of materials and deprived home backgrounds on school performance. There are also other basic data fields in the school master list that could provide the means to answer some of the most frequently asked questions about learner enrolment, teachers and learner-teacher ratio of schools. It is a useful dataset for education planners and researchers and is even widely used in the private sector by those who regularly deal with schools.

Geographic coverage

The data has national coverage

Analysis unit

Individuals and institutions

Universe

The survey covers all schools (ordinary and special needs) in South Africa, both public and independent.

Kind of data

Administrative records and survey data

Mode of data collection

Other

Research instrument

Data from the SNAP survey and ANA that are used to compile the Master List of Schools is collected with a survey questionnaire and educator forms. The principle completes the survey questionnaire and each educator (both state paid and other) in each school completes an educator form. Schools record their EMIS number provided by the DBE on the questionnaire and form for identification.

Data appraisal

The 2023 series only includes data for quarter 2 and quarter 3. The GIS coordinates for schools in the Eastern Cape are incorrectly entered in the original data from the DBE. The data entered in the GIS_long variable is incorrectly entered into the GIS_lat variable. This issue only occurs for schools in the Eastern Cape (EC), all other GIS coordinates for all the other provinces is correct. Therefore, for geospatial analysis, users can swap the GIS coordiate data only for the Eastern Cape.

Schools in all Pacific Island Countries and Territories have been included in their respective Education Management Information Systems in 2015 by the Statistics for Development Division of SPC. This data can be used for applications such as disaster mitigation and optimise emergency response and service delivery.

In response to the growing concern in geographic information science, which pertains to utilizing contemporary internet technology to communicate past information or knowledge for establishing foundations in geography. Recent studies have investigated geomatics solutions for historical city, and enhancing GIS skills through collaborative approach. In this study, we build upon prior research by exploring how the implementation of current technology can promote a cooperative learning environment, particularly within the realm of forestry education. Minetest, the 3D voxel game engine has high capability of modification, for visualizing natural environments and urban structures. The goal of this study was to investigate the potential of using the game engine for forestry education purposes. To meet this objective, we developed precise and detailed models of building structures and their surrounding environment. We also explored the visualization beyond 3D geospatial data, by generating analytical results of solar radiation on building roofs using GIS software. The visualization process was facilitated by the use of 3D light detection and ranging (LiDAR) data, provided by the UBC Campus + Community Planning department. The proposed approach proved to be effective in producing compatible geospatial data for visualization in the game engine. We also conducted exploratory statistical analysis to examine the relationship between building energy usage and solar radiation. The exploratory regression result of the solar radiation analysis has an R2adj of 0.19, which indicates its insignificant impact on building energy usage. Finally, the findings of this research provide a foundation for future studies that can continue to explore the potential of using 3D game engines. Keywords: 3D Geo-Visualization, Forestry Education, Remote Sensing, Light Detection and Ranging (LiDAR), Building Energy Usage, Solar Radiation Analysis

This dataset is from a master's group thesis project at The Bren School of Environmental Science & Management at the University of California, Santa Barbara, and contains the data files from the analyses (see below for the associated dataset containing the full final written report, which will provide more information on methods and results). The graduate student researchers who completed this project include: Tess Hooper, Daphne Virlar-Knight, Priscilla Hare, Robert Heim, and Jessica Gomez. This project, titled "Managing the Impacts of Environmental Education on Nature Preserves", focused on analyzing the potential ecological impacts that environmental education programming may have on sensitive plant and wildlife species on the Dangermond Preserve. Through mapping areas with sensitive plant and wildlife habitat, the group ranked environmental education trails on the preserve based on ecological impact. They also created a management tool that The Nature Conservancy can use to select trails that provide educational opportunities while reducing impacts to native biodiversity. Notably, the data indicates that all 12 education trails on the preserve pass through areas of low and high ecological impact, and that the best trail depends on each school group’s needs and The Nature Conservancy’s conservation goals. This project intends to help The Nature Conservancy manage its education programs on the Dangermond Preserve, and offers an approach that other land managers can use to inform decisions about balancing the trade-offs of environmental education in biologically diverse areas. The raw data used in the spatial analyses came from The Nature Conservancy and the following open source databases: 1. California Wildlife Habitat Relationship Systems 2. State Soil Geographic (STATSGO2) Data Base for California 3. 2014 California Basin Characteristic Model Downscaled Climate and Hydrology (30-year summaries) 4. Consortium of California Herbaria The project began in April 2019 and ended in June 2020. To access the final written report associated with this project, visit the dataset titled "Managing the Ecological Impacts of Environmental Education on the Dangermond Preserve (Final Report Only), The Bren School, University of California Santa Barbara, 2019-2020".

Count of students in each grade (PK-12) for each Alaska public school district. These data are taken from the official October 1 student count. This data set features historical data from the 2012-2013 school year to the present.Select 'Open in Map Viewer', or add this data to the Build Your Own Map application. From the Layer List, expand this map service to change what is visible on the map.Source: Alaska Department of Education & Early DevelopmentThis data has been visualized in a Geographic Information Systems (GIS) format and is provided as a service in the DCRA Information Portal by the Alaska Department of Commerce, Community, and Economic Development Division of Community and Regional Affairs (SOA DCCED DCRA), Research and Analysis section. SOA DCCED DCRA Research and Analysis is not the authoritative source for this data. For more information and for questions about this data, see: Alaska Department of Education & Early Development Data Center.

Summary data for coverage of legally mandated MPAs in the Coral Triangle countries (June 2013). (DOCX)

CSCD dateset for crop types classification based on multi-source high resolution remote sensing images

Author: Joseph Kerski, post_secondary_educator, Esri and University of DenverGrade/Audience: high school, ap human geography, post secondary, professional developmentResource type: lessonSubject topic(s): population, maps, citiesRegion: africa, asia, australia oceania, europe, north america, south america, united states, worldStandards: All APHG population tenets. Geography for Life cultural and population geography standards. Objectives: 1. Understand how population change and demographic characteristics are evident at a variety of scales in a variety of places around the world. 2. Understand the whys of where through analysis of change over space and time. 3. Develop skills using spatial data and interactive maps. 4. Understand how population data is communicated using 2D and 3D maps, visualizations, and symbology. Summary: Teaching and learning about demographics and population change in an effective, engaging manner is enriched and enlivened through the use of web mapping tools and spatial data. These tools, enabled by the advent of cloud-based geographic information systems (GIS) technology, bring problem solving, critical thinking, and spatial analysis to every classroom instructor and student (Kerski 2003; Jo, Hong, and Verma 2016).