Geospatial Solutions Market size was valued at USD 282.75 Billion in 2024 and is projected to reach USD 650.14 Billion by 2032, growing at a CAGR of 12.10% during the forecast period 2026-2032.

Geospatial Solutions Market: Definition/ Overview

Geospatial solutions are applications and technologies that use spatial data to address geography, location, and Earth's surface problems. They use tools like GIS, remote sensing, GPS, satellite imagery analysis, and spatial modelling. These solutions enable informed decision-making, resource allocation optimization, asset management, environmental monitoring, infrastructure planning, and addressing challenges in sectors like urban planning, agriculture, transportation, disaster management, and natural resource management. They empower users to harness spatial information for better understanding and decision-making in various contexts.

Geospatial solutions are technologies and methodologies used to analyze and visualize spatial data, ranging from urban planning to agriculture. They use GIS, remote sensing, and GNSS to gather, process, and interpret data. These solutions help users make informed decisions, solve complex problems, optimize resource allocation, and enhance situational awareness. They are crucial in addressing challenges and unlocking opportunities in today's interconnected world, such as mapping land use patterns, monitoring ecosystem changes, and real-time asset tracking.

Author: A Lisson, educator, Minnesota Alliance for Geographic EducationGrade/Audience: grade 8Resource type: lessonSubject topic(s): gis, geographic thinkingRegion: united statesStandards: Minnesota Social Studies Standards

Standard 1. People use geographic representations and geospatial technologies to acquire, process and report information within a spatial context.Objectives: Students will be able to:

1. Explain the difference between two types of geospatial technologies - GPS and GIS.
2. Develop basic skills to effectively manipulate and use GPS receivers and ArcGIS software.
3. Explain uses of GPS and GIS.Summary: Students use GPS coordinates to discover geocaches at a local park, and they use ArcGIS to layer maps about the park. Frontenac State park is the example, but any park or area (including school grounds) could be used. Students also investigate careers that use GIS.

This quadratic mean diameter (QMD) map (1:100,000) was prepared for the Tongass National Forest to provide up-to-date and more complete information about forest structure and patterns across the Ketchikan Misty Fjords Project Area. Over 3 million acres were mapped through a partnership between the Geospatial Technology and Applications Center (GTAC), Tongass National Forest, and the Alaska Regional Office. The Tongass National Forest and their partners prepared the regional classification system, identified the desired map units (map classes) and provided general project management. GTAC provided project support and expertise in vegetation mapping.This layer was developed for areas classified as forest on the final vegetation type map layer. QMD was assigned to modeling units (mapping polygons) using LiDAR data and predictive classification models. The minimum map feature depicted on the map is 0.25 acres. All map products were designed according to the Forest Service mid-level vegetation mapping standards in order to be stored in the Forest GIS and National databases. This map product was generated using imagery primarily acquired in 2019 - 2022 and LiDAR data from 2018. Therefore, the final map can be considered indicative of the existing vegetation conditions found within the project boundary in 2022.Note that the forest structure metrics will be more reliable within the LiDAR extent versus outside. Outside the LiDAR extent these metrics were extrapolated using coarser spectral and topographic data. Therefore, it is important for a product user to understand whether they are within the LiDAR acquisition area as indicated by the "Source_Extent" field in the geodatabase to ascertain the ultimate reliability of a particular structure metric.For more detailed information on mapping methodology please see the Ketchikan Misty Fjords Existing Vegetation Project Report.

The High Definition (HD) Maps market is experiencing significant growth, driven by the burgeoning autonomous vehicle (AV) sector and the increasing demand for advanced driver-assistance systems (ADAS). The market, estimated at $5 billion in 2025, is projected to exhibit a robust Compound Annual Growth Rate (CAGR) of 25% from 2025 to 2033, reaching an estimated market value of $25 billion by 2033. This expansion is fueled by several key factors. Firstly, the continuous development and deployment of autonomous vehicles necessitate highly accurate and detailed map data for safe and efficient navigation. Secondly, the increasing sophistication of ADAS features, such as lane-keeping assist and adaptive cruise control, rely heavily on precise HD map information. Thirdly, the rising adoption of connected car technologies further propels demand for HD maps, enabling real-time traffic updates, improved route planning, and enhanced in-car infotainment experiences. Major players like TomTom, Google, Here Technologies, and Baidu Apollo are actively investing in research and development to improve map accuracy, update frequency, and data integration capabilities, fostering fierce competition and driving innovation within the market. However, challenges remain. High initial investment costs for HD map creation and maintenance represent a significant barrier to entry for smaller companies. Furthermore, data security and privacy concerns surrounding the collection and use of location data need careful consideration and robust regulatory frameworks. The need for constant map updates to reflect dynamic road conditions and infrastructure changes presents an ongoing operational challenge. Despite these hurdles, the long-term outlook for the HD Maps market remains exceptionally positive, with continued technological advancements and increasing adoption across various sectors promising substantial future growth. The market is segmented geographically, with North America and Europe currently holding the largest market share, but rapidly expanding markets in Asia-Pacific are poised for significant future growth due to the rising adoption of connected and autonomous vehicles in those regions.

ArcGIS Online (AGOL) Feature Layer which includes the MDOT SHA 2015 Mean Higher High Water 4% Annual Chance (25YR Storm) - Depth Grid geospatial data product.MDOT SHA 2050 Mean Higher High Water 4% Annual Chance (25YR Storm) - Depth Grid consists of a depth grid image service depicting conditions of mean higher high water based on the 4% annual chance (25-Year Storm) event for coastal areas throughout the State of Maryland in year 2050. This data product supports Maryland Department of Transportation State Highway Administration (MDOT SHA) leadership and planners as they endeavor to mitigate or prevent the impacts of sea level change resulting from land surface subsidence and rising sea levels.MDOT SHA 2050 Mean Higher High Water 4% Annual Chance (25YR Storm) - Depth Grid data was produced as a result of efforts by the Maryland Department of Transportation State Highway Administration (MDOT SHA), Eastern Shore Regional GIS Cooperative (ESRGC), Salisbury University (SU), United States Corps of Engineers (USACE), National Oceanic & Atmospheric Administration (NOAA), and the United States Geological Survey (USGS). The US Army Corps of Engineers provide the sea level change estimate. Sea level change is localized using water elevations collected from a qualifying National Oceanic and Atmospheric Administration (NOAA) tidal reference station - NOAA observations are transformed from tidal datum to North American Vertical Datum of 1988. A final correction for glacial isostatic adjustment and land creates an sea level change value for the official project year, 2050.MDOT SHA 2050 Mean Higher High Water 4% Annual Chance (25YR Storm) - Depth Grid data was task-based, and will only be updated on an As-Needed basis where necessary.Last Updated: 10/07/2019For additional information, contact the MDOT SHA Geospatial Technologies:Email: GIS@mdot.maryland.govFor information related to the data, visit the Eastern Shore Regional GIS Cooperative (ESRGC) websiteWebsite: https:www.esrgc.org/mapServices/For additional data, visit the MDOT GIS Open Data Portal:Website: https://data.imap.maryland.gov/pages/mdot/For additional information related to the Maryland Department of Transportation (MDOT):Website: https://www.mdot.maryland.gov/For additional information related to the Maryland Department of Transportation State Highway Administration (MDOT SHA):Website: https://www.roads.maryland.gov/Home.aspxMDOT SHA Geospatial Data Legal Disclaimer:The Maryland Department of Transportation State Highway Administration (MDOT SHA) makes no warranty, expressed or implied, as to the use or appropriateness of geospatial data, and there are no warranties of merchantability or fitness for a particular purpose or use. The information contained in geospatial data is from publicly available sources, but no representation is made as to the accuracy or completeness of geospatial data. MDOT SHA shall not be subject to liability for human error, error due to software conversion, defect, or failure of machines, or any material used in the connection with the machines, including tapes, disks, CD-ROMs or DVD-ROMs and energy. MDOT SHA shall not be liable for any lost profits, consequential damages, or claims against MDOT SHA by third parties.

ArcGIS Online (AGOL) Feature Layer which includes the MDOT SHA 2100 Mean Sea Level 1% Annual Chance (100YR Storm) - Depth Grid geospatial data product.MDOT SHA 2100 Mean Sea Level 1% Annual Chance (100YR Storm) - Depth Grid consists of a depth grid image service depicting conditions of sea level change based on the 1% annual chance event (100-Year Storm) scenario for coastal areas throughout the State of Maryland in year 2100. This data product supports Maryland Department of Transportation State Highway Administration (MDOT SHA) leadership and planners as they endeavor to mitigate or prevent the impacts of sea level change resulting from land surface subsidence and rising sea levels.MDOT SHA 2100 Mean Sea Level 1% Annual Chance (100YR Storm) - Depth Grid data was produced as a result of efforts by the Maryland Department of Transportation State Highway Administration (MDOT SHA), Eastern Shore Regional GIS Cooperative (ESRGC), Salisbury University (SU), United States Corps of Engineers (USACE), National Oceanic & Atmospheric Administration (NOAA), and the United States Geological Survey (USGS). The US Army Corps of Engineers provide the sea level change estimate. Sea level change is localized using water elevations collected from a qualifying National Oceanic and Atmospheric Administration (NOAA) tidal reference station - NOAA observations are transformed from tidal datum to North American Vertical Datum of 1988. A final correction for glacial isostatic adjustment and land creates an sea level change value for the official project year, 2100.MDOT SHA 2100 Mean Sea Level 1% Annual Chance (100YR Storm) - Depth Grid data was task-based, and will only be updated on an As-Needed basis where necessary.Last Updated: 10/09/2019For additional information, contact the MDOT SHA Geospatial Technologies:Email: GIS@mdot.maryland.govFor information related to the data, visit the Eastern Shore Regional GIS Cooperative (ESRGC) websiteWebsite: https:www.esrgc.org/mapServices/For additional data, visit the MDOT GIS Open Data Portal:Website: https://data.imap.maryland.gov/pages/mdot/For additional information related to the Maryland Department of Transportation (MDOT):Website: https://www.mdot.maryland.gov/For additional information related to the Maryland Department of Transportation State Highway Administration (MDOT SHA):Website: https://www.roads.maryland.gov/Home.aspxMDOT SHA Geospatial Data Legal Disclaimer:The Maryland Department of Transportation State Highway Administration (MDOT SHA) makes no warranty, expressed or implied, as to the use or appropriateness of geospatial data, and there are no warranties of merchantability or fitness for a particular purpose or use. The information contained in geospatial data is from publicly available sources, but no representation is made as to the accuracy or completeness of geospatial data. MDOT SHA shall not be subject to liability for human error, error due to software conversion, defect, or failure of machines, or any material used in the connection with the machines, including tapes, disks, CD-ROMs or DVD-ROMs and energy. MDOT SHA shall not be liable for any lost profits, consequential damages, or claims against MDOT SHA by third parties.

Esri ArcGIS Online (AGOL) Imagery Layer which includes the MDOT SHA 2050 Mean Sea Level 10% Annual Chance (10.Year Storm) - Flood Depth Grid geospatial data product.MDOT SHA 2050 Mean Sea Level 10% Annual Chance (10.Year Storm) - Flood Depth Grid consists of a depth grid image service depicting conditions of sea level change based on the 10% annual chance event (10-Year Storm) scenario for coastal areas throughout the State of Maryland in year 2050. This data product supports Maryland Department of Transportation State Highway Administration (MDOT SHA) leadership and planners as they endeavor to mitigate or prevent the impacts of sea level change resulting from land surface subsidence and rising sea levels.MDOT SHA 2050 Mean Sea Level 10% Annual Chance (10.Year Storm) - Flood Depth Grid data was produced as a result of efforts by the Maryland Department of Transportation State Highway Administration (MDOT SHA), Eastern Shore Regional GIS Cooperative (ESRGC), Salisbury University (SU), United States Corps of Engineers (USACE), National Oceanic & Atmospheric Administration (NOAA), and the United States Geological Survey (USGS). The US Army Corps of Engineers provide the sea level change estimate. Sea level change is localized using water elevations collected from a qualifying National Oceanic and Atmospheric Administration (NOAA) tidal reference station - NOAA observations are transformed from tidal datum to North American Vertical Datum of 1988. A final correction for glacial isostatic adjustment and land creates an sea level change value for the official project year, 2050.MDOT SHA 2050 Mean Sea Level 10% Annual Chance (10.Year Storm) - Flood Depth Grid data was task-based, and will only be updated on an As-Needed basis where necessary.Last Updated: 10/07/2019For additional information, contact the MDOT SHA Geospatial Technologies:Email: GIS@mdot.maryland.govFor information related to the data, visit the Eastern Shore Regional GIS Cooperative (ESRGC) websiteWebsite: https:www.esrgc.org/mapServices/MDOT SHA Geospatial Data Legal Disclaimer:The Maryland Department of Transportation State Highway Administration (MDOT SHA) makes no warranty, expressed or implied, as to the use or appropriateness of geospatial data, and there are no warranties of merchantability or fitness for a particular purpose or use. The information contained in geospatial data is from publicly available sources, but no representation is made as to the accuracy or completeness of geospatial data. MDOT SHA shall not be subject to liability for human error, error due to software conversion, defect, or failure of machines, or any material used in the connection with the machines, including tapes, disks, CD-ROMs or DVD-ROMs and energy. MDOT SHA shall not be liable for any lost profits, consequential damages, or claims against MDOT SHA by third parties.

ArcGIS Online (AGOL) Feature Layer which includes the MDOT SHA 2050 Mean Higher High Water 10% Annual Chance (10YR Storm) - Depth Grid geospatial data product.MDOT SHA 2050 Mean Higher High Water 10% Annual Chance (10YR Storm) - Depth Grid consists of a depth grid image service depicting conditions of mean higher high water based on the 10% annual chance (10-Year Storm) event for coastal areas throughout the State of Maryland in year 2050. This data product supports Maryland Department of Transportation State Highway Administration (MDOT SHA) leadership and planners as they endeavor to mitigate or prevent the impacts of sea level change resulting from land surface subsidence and rising sea levels.MDOT SHA 2050 Mean Higher High Water 10% Annual Chance (10YR Storm) - Depth Grid data was produced as a result of efforts by the Maryland Department of Transportation State Highway Administration (MDOT SHA), Eastern Shore Regional GIS Cooperative (ESRGC), Salisbury University (SU), United States Corps of Engineers (USACE), National Oceanic & Atmospheric Administration (NOAA), and the United States Geological Survey (USGS). The US Army Corps of Engineers provide the sea level change estimate. Sea level change is localized using water elevations collected from a qualifying National Oceanic and Atmospheric Administration (NOAA) tidal reference station - NOAA observations are transformed from tidal datum to North American Vertical Datum of 1988. A final correction for glacial isostatic adjustment and land creates an sea level change value for the official project year, 2050.MDOT SHA 2050 Mean Higher High Water 10% Annual Chance (10YR Storm) - Depth Grid data was task-based, and will only be updated on an As-Needed basis where necessary.Last Updated: 10/07/2019For additional information, contact the MDOT SHA Geospatial Technologies:Email: GIS@mdot.maryland.govFor information related to the data, visit the Eastern Shore Regional GIS Cooperative (ESRGC) websiteWebsite: https:www.esrgc.org/mapServices/For additional data, visit the MDOT GIS Open Data Portal:Website: https://data.imap.maryland.gov/pages/mdot/For additional information related to the Maryland Department of Transportation (MDOT):Website: https://www.mdot.maryland.gov/For additional information related to the Maryland Department of Transportation State Highway Administration (MDOT SHA):Website: https://www.roads.maryland.gov/Home.aspxMDOT SHA Geospatial Data Legal Disclaimer:The Maryland Department of Transportation State Highway Administration (MDOT SHA) makes no warranty, expressed or implied, as to the use or appropriateness of geospatial data, and there are no warranties of merchantability or fitness for a particular purpose or use. The information contained in geospatial data is from publicly available sources, but no representation is made as to the accuracy or completeness of geospatial data. MDOT SHA shall not be subject to liability for human error, error due to software conversion, defect, or failure of machines, or any material used in the connection with the machines, including tapes, disks, CD-ROMs or DVD-ROMs and energy. MDOT SHA shall not be liable for any lost profits, consequential damages, or claims against MDOT SHA by third parties.

The high-precision mapping market is experiencing robust growth, driven by the increasing adoption of autonomous vehicles (AVs), advanced driver-assistance systems (ADAS), and the burgeoning need for precise location data across various industries. The market, estimated at $2 billion in 2025, is projected to experience a Compound Annual Growth Rate (CAGR) of approximately 20% during the forecast period (2025-2033), reaching an estimated market value of $10 billion by 2033. This significant expansion is fueled by several key factors, including the continuous improvement in sensor technology (LiDAR, radar, cameras), the development of sophisticated mapping algorithms and AI-powered data processing, and increasing government investments in smart city initiatives. Major players like HERE, TomTom, and Mobileye are leading the charge, constantly innovating and expanding their offerings to cater to the growing demand. However, challenges remain, including the high cost of data acquisition and processing, the need for robust data security and privacy measures, and the complexity of creating and maintaining accurate, up-to-date maps across diverse geographical locations. The segmentation of the high-precision mapping market reveals a diverse landscape, with key segments likely including mapping solutions for autonomous driving, ADAS, robotics, and urban planning. Regional growth is anticipated to be largely driven by North America and Europe, given the significant investments in autonomous vehicle technology and well-established infrastructure in these regions. However, other regions, such as Asia-Pacific, are expected to show substantial growth as investment in autonomous driving and smart city initiatives accelerates. The competitive landscape is dynamic, with both established players and emerging startups vying for market share. Strategic partnerships and acquisitions are likely to play a crucial role in shaping the future of this rapidly evolving market. The continued development of 5G and edge computing technologies will further accelerate innovation and create new opportunities for growth within this sector.

ArcGIS Online (AGOL) Feature Layer which includes the MDOT SHA 2100 Mean Sea Level 10% Annual Chance (10YR Storm) - Depth Grid geospatial data product.MDOT SHA 2100 Mean Sea Level 10% Annual Chance (10YR Storm) - Depth Grid consists of a depth grid image service depicting conditions of sea level change based on the 10% annual chance event (10-Year Storm) scenario for coastal areas throughout the State of Maryland in year 2100. This data product supports Maryland Department of Transportation State Highway Administration (MDOT SHA) leadership and planners as they endeavor to mitigate or prevent the impacts of sea level change resulting from land surface subsidence and rising sea levels.MDOT SHA 2100 Mean Sea Level 10% Annual Chance (10YR Storm) - Depth Grid data was produced as a result of efforts by the Maryland Department of Transportation State Highway Administration (MDOT SHA), Eastern Shore Regional GIS Cooperative (ESRGC), Salisbury University (SU), United States Corps of Engineers (USACE), National Oceanic & Atmospheric Administration (NOAA), and the United States Geological Survey (USGS). The US Army Corps of Engineers provide the sea level change estimate. Sea level change is localized using water elevations collected from a qualifying National Oceanic and Atmospheric Administration (NOAA) tidal reference station - NOAA observations are transformed from tidal datum to North American Vertical Datum of 1988. A final correction for glacial isostatic adjustment and land creates an sea level change value for the official project year, 2100.MDOT SHA 2100 Mean Sea Level 10% Annual Chance (10YR Storm) - Depth Grid data was task-based, and will only be updated on an As-Needed basis where necessary.Last Updated: 10/09/2019For additional information, contact the MDOT SHA Geospatial Technologies:Email: GIS@mdot.maryland.govFor information related to the data, visit the Eastern Shore Regional GIS Cooperative (ESRGC) websiteWebsite: https:www.esrgc.org/mapServices/For additional data, visit the MDOT GIS Open Data Portal:Website: https://data.imap.maryland.gov/pages/mdot/For additional information related to the Maryland Department of Transportation (MDOT):Website: https://www.mdot.maryland.gov/For additional information related to the Maryland Department of Transportation State Highway Administration (MDOT SHA):Website: https://www.roads.maryland.gov/Home.aspxMDOT SHA Geospatial Data Legal Disclaimer:The Maryland Department of Transportation State Highway Administration (MDOT SHA) makes no warranty, expressed or implied, as to the use or appropriateness of geospatial data, and there are no warranties of merchantability or fitness for a particular purpose or use. The information contained in geospatial data is from publicly available sources, but no representation is made as to the accuracy or completeness of geospatial data. MDOT SHA shall not be subject to liability for human error, error due to software conversion, defect, or failure of machines, or any material used in the connection with the machines, including tapes, disks, CD-ROMs or DVD-ROMs and energy. MDOT SHA shall not be liable for any lost profits, consequential damages, or claims against MDOT SHA by third parties.

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.

AbstractThe spatial and temporal distribution patterns of the livestock health status in the developing countrieslike India are complex. In this regards, the application of Geographical Information System (GIS) isvaluable as it has many features that make it an ideal tool for use in animal health surveillance, monitoring,prediction and its management strategy. The goal of the present study is to find out the effect of ambienttemperature on goat health in two different agro-climatic zones in West Bengal, India with the additionalhelp of GIS technology. The highest mean value of temperature (42.6 ± 1.5 ºC) has been reported duringthe month of April or May in the season of pre-monsoon in Purulia. Survey of India (SOI) topographicalsheets (73 I/3 and 79 B/5) are used to map the study areas. Top sheets are scanned, geo-referenced andthen digitized with the help of GIS software. The biochemical and meteorological data are entered to thenewly prepared digitized map as the non-spatial data or attributes. Moreover, the present work aims toconfer an indication of the potential applications and usages of a GIS in the field of animal health foradvancing the knowledge about this innovative approach of goat heath surveillance and monitoring.Keywords: Goats; GIS; Pre-Monsoon; Post-Monsoon; Purulia; Nadia.

ArcGIS Online (AGOL) Feature Layer which includes the MDOT SHA 2050 Mean Higher High Water 2% Annual Chance (50YR Storm) - Depth Grid geospatial data product.MDOT SHA 2050 Mean Higher High Water 2% Annual Chance (50YR Storm) - Depth Grid consists of a depth grid image service depicting conditions of mean higher high water based on the 2% annual chance (50-Year Storm) event for coastal areas throughout the State of Maryland in year 2050. This data product supports Maryland Department of Transportation State Highway Administration (MDOT SHA) leadership and planners as they endeavor to mitigate or prevent the impacts of sea level change resulting from land surface subsidence and rising sea levels.MDOT SHA 2050 Mean Higher High Water 2% Annual Chance (50YR Storm) - Depth Grid data was produced as a result of efforts by the Maryland Department of Transportation State Highway Administration (MDOT SHA), Eastern Shore Regional GIS Cooperative (ESRGC), Salisbury University (SU), United States Corps of Engineers (USACE), National Oceanic & Atmospheric Administration (NOAA), and the United States Geological Survey (USGS). The US Army Corps of Engineers provide the sea level change estimate. Sea level change is localized using water elevations collected from a qualifying National Oceanic and Atmospheric Administration (NOAA) tidal reference station - NOAA observations are transformed from tidal datum to North American Vertical Datum of 1988. A final correction for glacial isostatic adjustment and land creates an sea level change value for the official project year, 2050.MDOT SHA 2050 Mean Higher High Water 2% Annual Chance (50YR Storm) - Depth Grid data was task-based, and will only be updated on an As-Needed basis where necessary.Last Updated: 10/07/2019For additional information, contact the MDOT SHA Geospatial Technologies:Email: GIS@mdot.maryland.govFor information related to the data, visit the Eastern Shore Regional GIS Cooperative (ESRGC) websiteWebsite: https:www.esrgc.org/mapServices/For additional data, visit the MDOT GIS Open Data Portal:Website: https://data.imap.maryland.gov/pages/mdot/For additional information related to the Maryland Department of Transportation (MDOT):Website: https://www.mdot.maryland.gov/For additional information related to the Maryland Department of Transportation State Highway Administration (MDOT SHA):Website: https://www.roads.maryland.gov/Home.aspxMDOT SHA Geospatial Data Legal Disclaimer:The Maryland Department of Transportation State Highway Administration (MDOT SHA) makes no warranty, expressed or implied, as to the use or appropriateness of geospatial data, and there are no warranties of merchantability or fitness for a particular purpose or use. The information contained in geospatial data is from publicly available sources, but no representation is made as to the accuracy or completeness of geospatial data. MDOT SHA shall not be subject to liability for human error, error due to software conversion, defect, or failure of machines, or any material used in the connection with the machines, including tapes, disks, CD-ROMs or DVD-ROMs and energy. MDOT SHA shall not be liable for any lost profits, consequential damages, or claims against MDOT SHA by third parties.

Author: A Hotaling, educator, Minnesota Alliance for Geographic EducationGrade/Audience: high schoolResource type: lessonSubject topic(s): waterRegion: united statesStandards: Minnesota Social Studies Standards

Standard 1. People use geographic representations and geospatial technologies to acquire, process and report information within a spatial context.

Standard 3. Places have physical characteristics (such as climate, topography and vegetation) and human characteristics (such as culture, population, political and economic systems).

Standard 9. The environment influences human actions; and humans both adapt to and change the environment. Objectives: Students will be able to:

1. Explain the water cycle
2. Describe how people can change a watershed.
3. Provide examples to protect water for future generations.
4. Describe the connection between food and water.
5. Explain how agriculture can change the landscape and bodies of water.
6. Describe and evaluate the Gulf of Mexico Dead Zone.
7. Provide possible solutions to the Gulf of Mexico Dead Zone.
8. Interpret maps and data.
9. Create maps.
10. Write expository paragraphs.
11. Define terms related to water.
12. Use cardinal directions accurately.
13. Identify types of bodies of water. Summary: The topics include cardinal directions, water bodies, water cycle, watershed and people and watersheds - specifically the impact of agriculture. The local examples used for this lesson are Minneapolis and the Minnehaha watershed, the the lesson could be applied to any community and watershed. The lesson may also be applied to students at other grade levels.

Prediction errors (RMSE, MAE) and runtime of the models for the next 6 hours prediction implemented on all features.

The GIS-based Time model of Gothenburg aims to map the process of urban development in Gothenburg since 1960 and in particular to document the changes in the spatial form of the city - streets, buildings and plots - through time. Major steps have in recent decades been taken when it comes to understanding how cities work. Essential is the change from understanding cities as locations to understanding them as flows (Batty 2013)1. In principle this means that we need to understand locations (or places) as defined by flows (or different forms of traffic), rather than locations only served by flows. This implies that we need to understand the built form and spatial structure of cities as a system, that by shaping flows creates a series of places with very specific relations to all other places in the city, which also give them very specific performative potentials. It also implies the rather fascinating notion that what happens in one place is dependent on its relation to all other places (Hillier 1996)2. Hence, to understand the individual place, we need a model of the city as a whole.

Extensive research in this direction has taken place in recent years, that has also spilled over to urban design practice, not least in Sweden, where the idea that to understand the part you need to understand the whole is starting to be established. With the GIS-based Time model for Gothenburg that we present here, we address the next challenge. Place is not only something defined by its spatial relation to all other places in its system, but also by its history, or its evolution over time. Since the built form of the city changes over time, often by cities growing but at times also by cities shrinking, the spatial relation between places changes over time. If cities tend to grow, and most often by extending their periphery, it means that most places get a more central location over time. If this is a general tendency, it does not mean that all places increase their centrality to an equal degree. Depending on the structure of the individual city’s spatial form, different places become more centrally located to different degrees as well as their relative distance to other places changes to different degrees. The even more fascinating notion then becomes apparent; places move over time! To capture, study and understand this, we need a "time model".

The GIS-based time model of Gothenburg consists of: • 12 GIS-layers of the street network, from 1960 to 2015, in 5-year intervals • 12 GIS-layers of the buildings from 1960 to 2015, in 5-year intervals - Please note that this dataset has been moved to a separate catalog post (https://doi.org/10.5878/t8s9-6y15) and unpublished due to licensing restrictions on its source dataset. • 12 GIS- layers of the plots from1960 to 2015, in 5-year intervals

In the GIS-based Time model, for every time-frame, the combination of the three fundamental components of spatial form, that is streets, plots and buildings, provides a consistent description of the built environment at that particular time. The evolution of three components can be studied individually, where one could for example analyze the changing patterns of street centrality over time by focusing on the street network; or, the densification processes by focusing on the buildings; or, the expansion of the city by way of occupying more buildable land, by focusing on plots. The combined snapshots of street centrality, density and land division can provide insightful observations about the spatial form of the city at each time-frame; for example, the patterns of spatial segregation, the distribution of urban density or the patterns of sprawl. The observation of how the interrelated layers of spatial form together evolved and transformed through time can provide a more complete image of the patterns of urban growth in the city.

The Time model was created following the principles of the model of spatial form of the city, as developed by the Spatial Morphology Group (SMoG) at Chalmers University of Technology, within the three-year research project ‘International Spatial Morphology Lab (SMoL)’.

The project is funded by Älvstranden Utveckling AB in the framework of a larger cooperation project called Fusion Point Gothenburg. The data is shared via SND to create a research infrastructure that is open to new study initiatives.

1. Batty, M. (2013), The New Science of Cities, Cambridge: MIT Press.
2. Hillier, B., (1996), Space Is the Machine. Cambridge: University of Cambridge

12 GIS-layers of the street network in Gothenburg, from 1960 to 2015, in 5-year intervals. File format: shapefile (.shp), MapinfoTAB (.TAB). The coordinate system used is SWEREF 99TM, EPSG:3006.

See the attached Technical Documentation for the description and further details on the production of the datasets. See the attached Report for the description of the related research project.

ArcGIS Online (AGOL) Feature Layer which includes the MDOT SHA 2015 Mean Sea Level 4% Annual Chance (25YR Storm) - Depth Grid geospatial data product.MDOT SHA 2015 Mean Sea Level 4% Annual Chance (25YR Storm) - Depth Grid consists of a depth grid image service depicting conditions of sea level change based on the 4% annual chance event (25-Year Storm) scenario for coastal areas throughout the State of Maryland in year 2015. This data product supports Maryland Department of Transportation State Highway Administration (MDOT SHA) leadership and planners as they endeavor to mitigate or prevent the impacts of sea level change resulting from land surface subsidence and rising sea levels.MDOT SHA 2015 Mean Sea Level 4% Annual Chance (25YR Storm) - Depth Grid data was produced as a result of efforts by the Maryland Department of Transportation State Highway Administration (MDOT SHA), Eastern Shore Regional GIS Cooperative (ESRGC), Salisbury University (SU), United States Corps of Engineers (USACE), National Oceanic & Atmospheric Administration (NOAA), and the United States Geological Survey (USGS). The US Army Corps of Engineers provide the sea level change estimate. Sea level change is localized using water elevations collected from a qualifying National Oceanic and Atmospheric Administration (NOAA) tidal reference station - NOAA observations are transformed from tidal datum to North American Vertical Datum of 1988. A final correction for glacial isostatic adjustment and land creates an sea level change value for the official project year, 2015.MDOT SHA 2015 Mean Sea Level 4% Annual Chance (25YR Storm) - Depth Grid data was task-based, and will only be updated on an As-Needed basis where necessary.Last Updated: 10/07/2019For additional information, contact the MDOT SHA Geospatial Technologies:Email: GIS@mdot.maryland.govFor information related to the data, visit the Eastern Shore Regional GIS Cooperative (ESRGC) websiteWebsite: https:www.esrgc.org/mapServices/For additional data, visit the MDOT GIS Open Data Portal:Website: https://data.imap.maryland.gov/pages/mdot/For additional information related to the Maryland Department of Transportation (MDOT):Website: https://www.mdot.maryland.gov/For additional information related to the Maryland Department of Transportation State Highway Administration (MDOT SHA):Website: https://www.roads.maryland.gov/Home.aspxMDOT SHA Geospatial Data Legal Disclaimer:The Maryland Department of Transportation State Highway Administration (MDOT SHA) makes no warranty, expressed or implied, as to the use or appropriateness of geospatial data, and there are no warranties of merchantability or fitness for a particular purpose or use. The information contained in geospatial data is from publicly available sources, but no representation is made as to the accuracy or completeness of geospatial data. MDOT SHA shall not be subject to liability for human error, error due to software conversion, defect, or failure of machines, or any material used in the connection with the machines, including tapes, disks, CD-ROMs or DVD-ROMs and energy. MDOT SHA shall not be liable for any lost profits, consequential damages, or claims against MDOT SHA by third parties.

Summary statistics of the periods January-June 2019 and January-June 2020 for each data type.

Modeling, using the best available data and methods, was conducted to determine the potential future exposure of each of the main Hawaiian Islands to multiple coastal hazards as a result of sea level rise. Three chronic flooding hazards were modeled by the University of Hawaii Coastal Geology Group (CGG): a. passive flooding, b. annual high wave flooding, and c. coastal erosion (see descriptions of individual hazard layers for further details). The footprint of these three hazards were combined by Tetra Tech, Inc. to define the projected extent of chronic flooding due to sea level rise, called the sea level rise exposure area (SLR-XA). Flooding in the SLR-XA is associated with long-term, chronic hazards punctuated by annual or more frequent flooding events. Each of these hazards were modeled for four future sea level rise scenarios: 0.5 foot, 1.1 foot, 2.0 feet and 3.2 feet based on the upper end of the IPCC AR5 RCP8.5 sea level rise scenario. This particular layer depicts SLR-XA using the 2.0-ft (0.5991-m) sea level rise scenario. While the RCP8.5 predicts that this scenario would be reached by the year 2075, questions remain around the exact timing of sea level rise and recent observations and projections suggest a sooner arrival.Assumptions and Limitations: The assumptions and limitations described for the three chronic flooding hazards apply to the SLR-XA. Not all hazards were modeled for each island due to limited historical information and geospatial data. The SLR-XA for the islands of Hawaii, Molokai, and Lanai is based on modeling passive flooding only. Additional studies would be needed to add the annual high wave flooding and coastal erosion to the SLR-XA for those islands.The SLR-XA is an overlay of three hazards and does not account for interactive nature of these hazards as would be expected by natural processes. As with the individual exposure models, the SLR-XA maps hazard exposure on the present landscape. The modeling does not account for future (unknown) land use changes, including any adaptation measures. The SLR-XA also does not include impacts from less frequent high wave events (e.g., a 1-in-10 year event), storm surge, or tsunami.For further information, please see the Hawaii Sea Level Rise Vulnerability and Adaptation Report:http://climateadaptation.hawaii.gov/wp-content/uploads/2017/12/SLR-Report_Dec2017.pdfModeling, using the best available data and methods, was conducted to determine the potential future exposure of each of the main Hawaiian Islands to multiple coastal hazards as a result of sea level rise. Three chronic flooding hazards were modeled by the University of Hawaii Coastal Geology Group (CGG): a. passive flooding, b. annual high wave flooding, and c. coastal erosion (see descriptions of individual hazard layers for further details). The footprint of these three hazards were combined by Tetra Tech, Inc. to define the projected extent of chronic flooding due to sea level rise, called the sea level rise exposure area (SLR-XA). Flooding in the SLR-XA is associated with long-term, chronic hazards punctuated by annual or more frequent flooding events. Each of these hazards were modeled for four future sea level rise scenarios: 0.5 foot, 1.1 foot, 2.0 feet and 3.2 feet based on the upper end of the IPCC AR5 RCP8.5 sea level rise scenario. This particular layer depicts SLR-XA using the 2.0-ft (0.5991-m) sea level rise scenario. While the RCP8.5 predicts that this scenario would be reached by the year 2075, questions remain around the exact timing of sea level rise and recent observations and projections suggest a sooner arrival.Assumptions and Limitations: The assumptions and limitations described for the three chronic flooding hazards apply to the SLR-XA. Not all hazards were modeled for each island due to limited historical information and geospatial data. The SLR-XA for the islands of Hawaii, Molokai, and Lanai is based on modeling passive flooding only. Additional studies would be needed to add the annual high wave flooding and coastal erosion to the SLR-XA for those islands.The SLR-XA is an overlay of three hazards and does not account for interactive nature of these hazards as would be expected by natural processes. As with the individual exposure models, the SLR-XA maps hazard exposure on the present landscape. The modeling does not account for future (unknown) land use changes, including any adaptation measures. The SLR-XA also does not include impacts from less frequent high wave events (e.g., a 1-in-10 year event), storm surge, or tsunami.For further information, please see the Hawaii Sea Level Rise Vulnerability and Adaptation Report:http://climateadaptation.hawaii.gov/wp-content/uploads/2017/12/SLR-Report_Dec2017.pdfModeling, using the best available data and methods, was conducted to determine the potential future exposure of each of the main Hawaiian Islands to multiple coastal hazards as a result of sea level rise. Three chronic flooding hazards were modeled by the University of Hawaii Coastal Geology Group (CGG): a. passive flooding, b. annual high wave flooding, and c. coastal erosion (see descriptions of individual hazard layers for further details). The footprint of these three hazards were combined by Tetra Tech, Inc. to define the projected extent of chronic flooding due to sea level rise, called the sea level rise exposure area (SLR-XA). Flooding in the SLR-XA is associated with long-term, chronic hazards punctuated by annual or more frequent flooding events. Each of these hazards were modeled for four future sea level rise scenarios: 0.5 foot, 1.1 foot, 2.0 feet and 3.2 feet based on the upper end of the IPCC AR5 RCP8.5 sea level rise scenario. This particular layer depicts SLR-XA using the 2.0-ft (0.5991-m) sea level rise scenario. While the RCP8.5 predicts that this scenario would be reached by the year 2075, questions remain around the exact timing of sea level rise and recent observations and projections suggest a sooner arrival.Assumptions and Limitations: The assumptions and limitations described for the three chronic flooding hazards apply to the SLR-XA. Not all hazards were modeled for each island due to limited historical information and geospatial data. The SLR-XA for the islands of Hawaii, Molokai, and Lanai is based on modeling passive flooding only. Additional studies would be needed to add the annual high wave flooding and coastal erosion to the SLR-XA for those islands.The SLR-XA is an overlay of three hazards and does not account for interactive nature of these hazards as would be expected by natural processes. As with the individual exposure models, the SLR-XA maps hazard exposure on the present landscape. The modeling does not account for future (unknown) land use changes, including any adaptation measures. The SLR-XA also does not include impacts from less frequent high wave events (e.g., a 1-in-10 year event), storm surge, or tsunami.For further information, please see the Hawaii Sea Level Rise Vulnerability and Adaptation Report:http://climateadaptation.hawaii.gov/wp-content/uploads/2017/12/SLR-Report_Dec2017.pdfModeling, using the best available data and methods, was conducted to determine the potential future exposure of each of the main Hawaiian Islands to multiple coastal hazards as a result of sea level rise. Three chronic flooding hazards were modeled by the University of Hawaii Coastal Geology Group (CGG): a. passive flooding, b. annual high wave flooding, and c. coastal erosion (see descriptions of individual hazard layers for further details). The footprint of these three hazards were combined by Tetra Tech, Inc. to define the projected extent of chronic flooding due to sea level rise, called the sea level rise exposure area (SLR-XA). Flooding in the SLR-XA is associated with long-term, chronic hazards punctuated by annual or more frequent flooding events. Each of these hazards were modeled for four future sea level rise scenarios: 0.5 foot, 1.1 foot, 2.0 feet and 3.2 feet based on the upper end of the IPCC AR5 RCP8.5 sea level rise scenario. This particular layer depicts SLR-XA using the 2.0-ft (0.5991-m) sea level rise scenario. While the RCP8.5 predicts that this scenario would be reached by the year 2075, questions remain around the exact timing of sea level rise and recent observations and projections suggest a sooner arrival.Assumptions and Limitations: The assumptions and limitations described for the three chronic flooding hazards apply to the SLR-XA. Not all hazards were modeled for each island due to limited historical information and geospatial data. The SLR-XA for the islands of Hawaii, Molokai, and Lanai is based on modeling passive flooding only. Additional studies would be needed to add the annual high wave flooding and coastal erosion to the SLR-XA for those islands.The SLR-XA is an overlay of three hazards and does not account for interactive nature of these hazards as would be expected by natural processes. As with the individual exposure models, the SLR-XA maps hazard exposure on the present landscape. The modeling does not account for future (unknown) land use changes, including any adaptation measures. The SLR-XA also does not include impacts from less frequent high wave events (e.g., a 1-in-10 year event), storm surge, or tsunami.For further information, please see the Hawaii Sea Level Rise Vulnerability and Adaptation Report:http://climateadaptation.hawaii.gov/wp-content/uploads/2017/12/SLR-Report_Dec2017.pdf

ArcGIS Online (AGOL) Feature Layer which includes the MDOT SHA 2015 Mean Sea Level 1% Annual Chance (100YR Storm) - Depth Grid geospatial data product.MDOT SHA 2015 Mean Sea Level 1% Annual Chance (100YR Storm) - Depth Grid consists of a depth grid image service depicting conditions of sea level change based on the 1% annual chance event (100-Year Storm) scenario for coastal areas throughout the State of Maryland in year 2015. This data product supports Maryland Department of Transportation State Highway Administration (MDOT SHA) leadership and planners as they endeavor to mitigate or prevent the impacts of sea level change resulting from land surface subsidence and rising sea levels.MDOT SHA 2015 Mean Sea Level 1% Annual Chance (100YR Storm) - Depth Grid data was produced as a result of efforts by the Maryland Department of Transportation State Highway Administration (MDOT SHA), Eastern Shore Regional GIS Cooperative (ESRGC), Salisbury University (SU), United States Corps of Engineers (USACE), National Oceanic & Atmospheric Administration (NOAA), and the United States Geological Survey (USGS). The US Army Corps of Engineers provide the sea level change estimate. Sea level change is localized using water elevations collected from a qualifying National Oceanic and Atmospheric Administration (NOAA) tidal reference station - NOAA observations are transformed from tidal datum to North American Vertical Datum of 1988. A final correction for glacial isostatic adjustment and land creates an sea level change value for the official project year, 2015.MDOT SHA 2015 Mean Sea Level 1% Annual Chance (100YR Storm) - Depth Grid data was task-based, and will only be updated on an As-Needed basis where necessary.Last Updated: 10/07/2019For additional information, contact the MDOT SHA Geospatial Technologies:Email: GIS@mdot.maryland.govFor information related to the data, visit the Eastern Shore Regional GIS Cooperative (ESRGC) websiteWebsite: https:www.esrgc.org/mapServices/For additional data, visit the MDOT GIS Open Data Portal:Website: https://data.imap.maryland.gov/pages/mdot/For additional information related to the Maryland Department of Transportation (MDOT):Website: https://www.mdot.maryland.gov/For additional information related to the Maryland Department of Transportation State Highway Administration (MDOT SHA):Website: https://www.roads.maryland.gov/Home.aspxMDOT SHA Geospatial Data Legal Disclaimer:The Maryland Department of Transportation State Highway Administration (MDOT SHA) makes no warranty, expressed or implied, as to the use or appropriateness of geospatial data, and there are no warranties of merchantability or fitness for a particular purpose or use. The information contained in geospatial data is from publicly available sources, but no representation is made as to the accuracy or completeness of geospatial data. MDOT SHA shall not be subject to liability for human error, error due to software conversion, defect, or failure of machines, or any material used in the connection with the machines, including tapes, disks, CD-ROMs or DVD-ROMs and energy. MDOT SHA shall not be liable for any lost profits, consequential damages, or claims against MDOT SHA by third parties.