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.

City of Amesbury, MA GIS Viewer

In Module 2 Lesson 1, we will take a deeper dive into Geographic Information Systems (GIS) technology. We'll explore different types of GIS data, the importance of data attributes and queries, data symbolization, and ways to access GIS technology.

These data provide an accurate high-resolution shoreline compiled from lidar and imagery of Nantucket Island, MA . This vector shoreline data is based on an office interpretation of imagery that may be suitable as a geographic information system (GIS) data layer. This metadata describes information for both the line and point shapefiles. The NGS attribution scheme 'Coastal Cartographic Object Attribute Source Table (C-COAST)' was developed to conform the attribution of various sources of shoreline data into one attribution catalog. C-COAST is not a recognized standard, but was influenced by the International Hydrographic Organization's S-57 Object-Attribute standard so the data would be more accurately translated into S-57. This resource is a member of https://www.fisheries.noaa.gov/inport/item/39808

Town of Middleton, MA GIS Viewer

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

City of Holyoke, MA GIS Viewer

MassGIS is working very closely with the State 911 Department in the state’s Executive Office of Public Safety and Security on the Next Generation 911 Emergency Call System. MassGIS developed and is maintaining the map and address information that are at the heart of this new system. Statewide deployment of this new 9-1-1 call routing system was completed in 2018.Address sources include the Voter Registration List from the Secretary of the Commonwealth, site addresses from municipal departments (primarily assessors), and customer address lists from utilities. Addresses from utilities were “anonymized” to protect customer privacy. The MAD was also validated for completeness using the Emergency Service List (a list of telephone land line addresses) from Verizon.The MAD contains both tabular and spatial data, with addresses being mapped as point features. At present, the MAD contains 3.2 million address records and 2.2 million address points. As the database is very dynamic with changes being made daily, the data available for download will be refreshed weekly.A Statewide Addressing Standard for Municipalities is another useful asset that has been created as part of this ongoing project. It is a best practices guide for the creation and storage of addresses for Massachusetts Municipalities.Points features with each point having an address to the building/floor/unit level, when that information is available. Where more than one address is located at a single location multiple points are included (i.e. "stacked points"). The points for the most part represent building centroids. Other points are located as assessor parcel centroids.Points will display at scales 1:75,000 and closer.MassGIS' service does not contain points for Boston; they may be accessed at https://data.boston.gov/dataset/live-street-address-management-sam-addresses/resource/873a7659-68b6-4ac0-98b7-6d8af762b6f1.More details about the MAD and Master Address Points.Map service also available.

These data were automated to provide an accurate high-resolution historical shoreline of Provincetown, MA suitable as a geographic information system (GIS) data layer. These data are derived from shoreline maps that were produced by the NOAA National Ocean Service including its predecessor agencies which were based on an office interpretation of imagery and/or field survey. The NGS attribut...

Northeastern United States State Boundary data are intended for geographic display of state boundaries at statewide and regional levels. Use it to map and label states on a map. These data are derived from Northeastern United States Political Boundary Master layer. This information should be displayed and analyzed at scales appropriate for 1:24,000-scale data. The State of Connecticut, Department of Environmental Protection (CTDEP) assembled this regional data layer using data from other states in order to create a single, seamless representation of political boundaries within the vicinity of Connecticut that could be easily incorporated into mapping applications as background information. More accurate and up-to-date information may be available from individual State government Geographic Information System (GIS) offices. Not intended for maps printed at map scales greater or more detailed than 1:24,000 scale (1 inch = 2,000 feet.)

These data were automated to provide an accurate high-resolution historical shoreline of Westport, MA suitable as a geographic information system (GIS) data layer. These data are derived from shoreline maps that were produced by the NOAA National Ocean Service including its predecessor agencies which were based on an office interpretation of imagery and/or field survey. The NGS attribution scheme 'Coastal Cartographic Object Attribute Source Table (C-COAST)' was developed to conform the attribution of various sources of shoreline data into one attribution catalog. C-COAST is not a recognized standard, but was influenced by the International Hydrographic Organization's S-57 Object-Attribute standard so the data would be more accurately translated into S-57. This resource is a member of https://inport.nmfs.noaa.gov/inport/item/39808

These data provide an accurate high-resolution shoreline compiled from imagery of Port of Pascagoula-Moss Point, MS . This vector shoreline data is based on an office interpretation of imagery that may be suitable as a geographic information system (GIS) data layer. This metadata describes information for both the line and point shapefiles. The NGS attribution scheme 'Coastal Cartographic Object Attribute Source Table (C-COAST)' was developed to conform the attribution of various sources of shoreline data into one attribution catalog. C-COAST is not a recognized standard, but was influenced by the International Hydrographic Organization's S-57 Object-Attribute standard so the data would be more accurately translated into S-57. This resource is a member of https://inport.nmfs.noaa.gov/inport/item/39808

Northeastern United States Town Boundary data are intended for geographic display of state, county and town (municipal) boundaries at statewide and regional levels. Use it to map and label towns on a map. These data are derived from Northeastern United States Political Boundary Master layer. This information should be displayed and analyzed at scales appropriate for 1:24,000-scale data. The State of Connecticut, Department of Environmental Protection (CTDEP) assembled this regional data layer using data from other states in order to create a single, seamless representation of political boundaries within the vicinity of Connecticut that could be easily incorporated into mapping applications as background information. More accurate and up-to-date information may be available from individual State government Geographic Information System (GIS) offices. Not intended for maps printed at map scales greater or more detailed than 1:24,000 scale (1 inch = 2,000 feet.)

These data were automated to provide an accurate high-resolution historical shoreline of Cape Cod Bay, MA suitable as a geographic information system (GIS) data layer. These data are derived from shoreline maps that were produced by the NOAA National Ocean Service including its predecessor agencies which were based on an office interpretation of imagery and/or field survey. The NGS attribution scheme 'Coastal Cartographic Object Attribute Source Table (C-COAST)' was developed to conform the attribution of various sources of shoreline data into one attribution catalog. C-COAST is not a recognized standard, but was influenced by the International Hydrographic Organization's S-57 Object-Attribute standard so the data would be more accurately translated into S-57. This resource is a member of https://inport.nmfs.noaa.gov/inport/item/39808

These data provide an accurate high-resolution shoreline compiled from imagery of MADAKET HARBOR, MA . This vector shoreline data is based on an office interpretation of imagery that may be suitable as a geographic information system (GIS) data layer. This metadata describes information for both the line and point shapefiles. The NGS attribution scheme 'Coastal Cartographic Object Attribute Source Table (C-COAST)' was developed to conform the attribution of various sources of shoreline data into one attribution catalog. C-COAST is not a recognized standard, but was influenced by the International Hydrographic Organization's S-57 Object-Attribute standard so the data would be more accurately translated into S-57. This resource is a member of https://www.fisheries.noaa.gov/inport/item/39808

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.

The Idaho boundary, taken from the Tiger lines file is used here for the purposes of creating a masking showing only data within the state of Idaho. This allows for the prioritization of mesic habitat within idaho.TIGER/Line Geodatabases are spatial extracts from the Census Bureau’s Master Address File/Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) System for use with geographic information systems (GIS) software. The geodatabases contain national coverage (for geographic boundaries or features) or state coverage (boundaries within state).https://www.census.gov/geographies/mapping-files/time-series/geo/tiger-geodatabase-file.html

The global geospatial analytics market is predicted to expand significantly, with a projected CAGR of 11.28% from 2025 to 2033. Valued at 89.23 billion USD in 2025, the market is expected to reach new heights during the forecast period. Key drivers fueling this growth include increasing adoption of GIS (Geographic Information Systems) and GPS (Global Positioning Systems), rising demand for location-based services, and growing awareness of the benefits of geospatial data in decision-making. Additionally, advancements in cloud computing, artificial intelligence, and machine learning further contribute to the market's expansion. Key segments in the geospatial analytics market include services, types, technologies, and regions. Consulting, integration and deployment, support and maintenance are prominent services offered in the market. Surface and field analytics, network and location analytics, geovisualization, and other types are also significant segments. Remote sensing GIS GPS, other technologies, and their applications across various regions, including North America, Europe, Asia Pacific, Middle East & Africa, and South America, shape the market dynamics. Recent developments include: Sept 2022 Sanborn Map Company Inc., a provider of geospatial solutions for government and commercial clients, has acquired Applied Geographics, Inc., which helped numerous organisations in finding the most effective GIS, location intelligence, and geospatial solutions., January 2022 With the help of integrated and improved data, ideal site analysis and path planning, and customized customer experiences, Blueprint Technologies and Precisely have announced a partnership to help businesses gain a competitive edge., Geospatial analytics is being used by telecom companies like T-Mobile to optimise coverage and quality of service while planning deployments. While organising service deployments and coverage, telecommunications providers must consider a wide range of criteria. They must take into account the varying usage patterns, service demands, and the dynamic nature of the areas they serve., According to industry analysts, the abundance of geospatial data accessible is outpacing people's capacity to comprehend it as government and business deploy more satellites, drones, and sensors than ever before. Artificial intelligence, according to Mark Munsell, Deputy Director for Data and Digital Innovation at the National Geospatial-Intelligence Agency., Geospatial intelligence experts Orbital Insight and Carahsoft Technologies Corp. have joined forces. Carahsoft will act as Orbital Insight's Master Government Aggregator in accordance with the agreement. Through Carahsoft's reseller partners, Information Technology Enterprise Solutions - Software 2 (ITES-SW2), NASA Solutions for Enterprise-Wide Procurement (SEWP) V, National Association of State Procurement Officials (NASPO), ValuePoint, National Cooperative Purchasing Alliance (NCPA), and OMNIA Partners contracts, the company's AI-powered geospatial data analytics are now accessible to the public sector.. Potential restraints include: High Initial Investment Cost.

This viewer displays MassGIS' statewide collection of the Basic address points with fields added to facilitate geocoding inside ArcMap. It includes point data for all communities from MassGIS' Master Address Database (MAD), plus points for Boston obtained from the city.The MAD is used for compiling, standardizing, editing and maintaining addresses for the Next Generation 911 system project. MassGIS' goal is to provide municipalities, residences, businesses, and application developers with the most comprehensive list of standardized addresses for cities and towns throughout the Commonwealth.Download Points by City/Town

These data provide an accurate high-resolution shoreline compiled from imagery of PORT OF BILOXI, MS . This vector shoreline data is based on an office interpretation of imagery that may be suitable as a geographic information system (GIS) data layer. This metadata describes information for both the line and point shapefiles. The NGS attribution scheme 'Coastal Cartographic Object Attribute Source Table (C-COAST)' was developed to conform the attribution of various sources of shoreline data into one attribution catalog. C-COAST is not a recognized standard, but was influenced by the International Hydrographic Organization's S-57 Object-Attribute standard so the data would be more accurately translated into S-57. This resource is a member of https://www.fisheries.noaa.gov/inport/item/39808