A web map used to access tax parcel, boundary, ownership, acreage, survey, zoning and tax information. Errors and Omissions Do Exist.The information provided is for reference only and subject to independent verification. User assumes all responsibility for its use.https://www.fayette-co-oh.com/Fayette County ProfileFayette County is a county located in the U.S. state of Ohio. Its county seat is Washington Court House. Fayette County was formed on March 1, 1810 from portions of Highland County and Ross County. It was named after Marie-Joseph Motier, Marquis de La Fayette, a French general and politician who took the side of the Colonials during the American Revolutionary War and who played an important role in the French Revolution.Fayette County is a part of the Virginia Military survey, which was reserved in 1783, to be allotted to Virginia soldiers. This district includes the entire counties of Adams, Brown, Clermont, Clinton, Highland, Fayette, Madison and Union; and a portion of the counties of Scioto, Pike, Ross, Pickaway, Franklin, Delaware, Marion, Hardin, Logan, Champaign, Clarke, Greene, Warren and Hamilton.Fayette County was formed January 19, 1810 (took effect March 1st) from Ross and Highland counties. Beginning at the southwest corner of Pickaway, running north “with the line of said county to the corner of Madison; thence west with the line of said Madison county to the line of Greene county; thence south with the line of Greene county to the southeast corner thereof; thence east five miles; thence south to the line of Highland county; thence east with said line to Paint Creek; thence in a straight line to the beginning.” All the lower portion was taken from Highland and the upper from Ross.The first portion of land entered within the territory of what is now Fayette county, was a part of original surveys Nos. 243 and 772, lying partly in Clinton county. The first survey lying wholly within Fayette county was No. 463, in what is now Madison township, surveyed for Thomas Overton by John O’Bannon June 30, 1776.The original townships were Jefferson, Greene, Wayne, Madison, Paint and Union. Concord township was formed in April 1818, from Greene. Marion township was formed in June, 1840 from Madison. Perry township was formed June 4, 1845, from Wayne and Greene. Jasper township was formed from Jefferson and Concord December 2, 1845.Washington C.H. was laid out originally on a part of entry 757, which contained 1200 acres and belonged to Benjamin Temple, of Logan county, Kentucky, who donated 150 acres to Fayette county, on condition that it be used as the site of the county seat. The deed of conveyance was made December 1, 1810, by Thomas S. Hind, attorney for Temple, to Robert Stewart, who was appointed by the legislature as director for the town of Washington. The town was laid off some time between December 1, 1810, and February 26, 1811, the latter being the date of the record of the town plat.Bloomingburg (originally called New Lexington) was laid out in 1815, by Solomon Bowers, and originally contained 34 and ¾ acres. On March 4, 1816, Bowers laid out and added twenty more lots. The name of the town was later changed to Bloomingburg by act of the legislature. The town was incorporated by act of the legislature, February 5, 1847.Jeffersonville was laid out March 1, 1831, by Walter B. Write and Chipman Robinson, on 100 acres of land belonging to them, they started selling the lots at $5 each. The town incorporated March 17, 1838. The first house was erected by Robert Wyley.The first railroad, now the C. & M. V., was completed in 1852; the second, now the Detroit Southern, in 1875; the third, now the C.H. & D. in 1879; and the fourth, now the B. & O. S. W., in 1884.The first permanent settler (probably) was a Mr. Wolf who settled in what is now Wayne township, in about the year 1796. - Circa 1886 - Map of Fayette County, Ohio. Issued by the Fayette County Record.

Geographic Information System (GIS) Market Outlook

The Geographic Information System (GIS) market is witnessing robust growth with its global market size projected to reach USD 25.7 billion by 2032, up from USD 8.7 billion in 2023, at a compound annual growth rate (CAGR) of 12.4% during the forecast period. This growth is primarily driven by the increasing integration of GIS technology across various industries to improve spatial data visualization, enhance decision-making, and optimize operations. The benefits offered by GIS in terms of accuracy, efficiency, and cost-effectiveness are convincing more sectors to adopt these systems, thereby expanding the market size significantly.

A major growth factor contributing to the GIS market expansion is the escalating demand for location-based services. As businesses across different sectors recognize the importance of spatial data analytics in driving strategic decisions, the reliance on GIS applications is becoming increasingly pronounced. The rise in IoT devices, coupled with the enhanced capabilities of AI and machine learning, has further fueled the demand for GIS solutions. These technologies enable the processing and analysis of large volumes of spatial data, thereby providing valuable insights that businesses can leverage for competitive advantage. In addition, government initiatives promoting the adoption of digital infrastructure and smart city projects are playing a crucial role in the growth of the GIS market.

The advancement in satellite imaging and remote sensing technologies is another key driver of the GIS market growth. With enhanced satellite capabilities, the precision and quality of geospatial data have significantly improved, making GIS applications more reliable and effective. The availability of high-resolution satellite imagery has opened new avenues in various sectors including agriculture, urban planning, and disaster management. Moreover, the decreasing costs of satellite data acquisition and the proliferation of drone technology are making GIS more accessible to small and medium enterprises, further expanding the market potential.

The advent of 3D Geospatial Technologies is revolutionizing the way industries utilize GIS data. By providing a three-dimensional perspective, these technologies enhance spatial analysis and visualization, offering more detailed and accurate representations of geographical areas. This advancement is particularly beneficial in urban planning, where 3D models can simulate cityscapes and infrastructure, allowing planners to visualize potential developments and assess their impact on the environment. Moreover, 3D geospatial data is proving invaluable in sectors such as construction and real estate, where it aids in site analysis and project planning. As these technologies continue to evolve, they are expected to play a pivotal role in the future of GIS, expanding its applications and driving further market growth.

Furthermore, the increasing application of GIS in environmental monitoring and management is bolstering market growth. With growing concerns over climate change and environmental degradation, GIS is being extensively used for resource management, biodiversity conservation, and natural disaster risk management. This trend is expected to continue as more organizations and governments prioritize sustainability, thereby driving the demand for advanced GIS solutions. The integration of GIS with other technologies such as big data analytics, and cloud computing is also expected to enhance its capabilities, making it an indispensable tool for environmental management.

Regionally, North America is currently leading the GIS market, driven by the widespread adoption of advanced technologies and the presence of major GIS vendors. The regionÂ’s focus on infrastructure development and smart city projects is further propelling the market growth. Europe is also witnessing significant growth owing to the increasing adoption of GIS in various industries such as agriculture and transportation. The Asia Pacific region is anticipated to exhibit the highest CAGR during the forecast period, attributed to rapid urbanization, government initiatives for digital transformation, and increasing investments in infrastructure development. In contrast, the markets in Latin America and the Middle East & Africa are growing steadily as these regions continue to explore and adopt GIS technologies.

<a href="https://dataintelo.com/report/geospatial-data-fusion-market" target="_blank&quo

This is an exercise on the use of Postal Code Conversion Files (PCCF) with GIS. (Note: Data associated with this exercise is available on the DLI FTP site under folder 1873-299.)

Discover the booming GIS software market! Explore its $15 billion valuation, 12% CAGR growth, key drivers, trends, and leading players like Esri & Google. This in-depth analysis reveals regional market share and future projections through 2033.

This point feature class is the message sign portion of the Intelligent Transportation System (ITS) which manages demand on the system and provides information to the public on traffic movement and delays.-- Additional Information: Category: Utilities - Communications Purpose: For mapping features in the Intelligent Transportation System. Update Frequency: As needed-- Metadata Link: https://www.portlandmaps.com/metadata/index.cfm?&action=DisplayLayer&LayerID=52788

For more information on Idaho Outfitters and Guides Licensing Board, visit: https://elitepublic.oglb.idaho.gov/OGLBPortal/To search for an outfitter or guide, visit: https://idfg.idaho.gov/ifwis/ioglb/. Please contact ITS GIS team for the maintenance of this dataset ITSGISTeam@its.idaho.gov.

Thinking Spatially Using GIS

Thinking Spatially Using GIS is a 1:1 set of instructional materials for students that use ArcGIS Online to teach basic geography concepts found in upper elementary school and above.
Each module has both a teacher and student file.

The United States population has grown quickly during the past several hundred years. Keeping track of the nation’s population dates to the country’s origins. The U.S. Constitution adopted in 1787 called for a population count every 10 years, starting in 1790. This process, called the census, would keep track of the population, its activities, and its movements. More importantly, the census would ensure that each state received fair and accurate representation in the U.S. House of Representatives.

The 1790 Census recorded almost 4 million people. By comparison, the 2000 Census counted almost 300 million. That’s more than 70 times the number of people that lived in the United States 210 years ago! It is estimated that by 2050 there will be 392 million people living in the United States! The United States now is the third most populated country in the world after China and India.

The Thinking Spatially Using GIS home is at: http://esriurl.com/TSG

All Esri GeoInquiries can be found at: http://www.esri.com/geoinquiries

DEM Bare Earth Tile Indexes for each LiDAR project. The “DIRECT_DL” field contains a hyperlink to download the associated IMG files. More information for existing DEM collections can be found at https://gis.ny.gov/nys-dem. Service last updated 9/5/25Feature and map services available:https://elevation.its.ny.gov/arcgis/rest/services/Dem_Indexes/FeatureServerhttps://elevation.its.ny.gov/arcgis/rest/services/Dem_Indexes/MapServer Please contact NYS ITS Geospatial Services at nysgis@its.ny.gov if you have any questions.

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GIS Market Size 2025-2029

The GIS market size is forecast to increase by USD 24.07 billion, at a CAGR of 20.3% between 2024 and 2029.

The Global Geographic Information System (GIS) market is experiencing significant growth, driven by the increasing integration of Building Information Modeling (BIM) and GIS technologies. This convergence enables more effective spatial analysis and decision-making in various industries, particularly in soil and water management. However, the market faces challenges, including the lack of comprehensive planning and preparation leading to implementation failures of GIS solutions. Companies must address these challenges by investing in thorough project planning and collaboration between GIS and BIM teams to ensure successful implementation and maximize the potential benefits of these advanced technologies.
By focusing on strategic planning and effective implementation, organizations can capitalize on the opportunities presented by the growing adoption of GIS and BIM technologies, ultimately driving operational efficiency and innovation.

What will be the Size of the GIS Market during the forecast period?

Explore in-depth regional segment analysis with market size data - historical 2019-2023 and forecasts 2025-2029 - in the full report.
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The global Geographic Information Systems (GIS) market continues to evolve, driven by the increasing demand for advanced spatial data analysis and management solutions. GIS technology is finding applications across various sectors, including natural resource management, urban planning, and infrastructure management. The integration of Bing Maps, terrain analysis, vector data, Lidar data, and Geographic Information Systems enables precise spatial data analysis and modeling. Hydrological modeling, spatial statistics, spatial indexing, and route optimization are essential components of GIS, providing valuable insights for sectors such as public safety, transportation planning, and precision agriculture. Location-based services and data visualization further enhance the utility of GIS, enabling real-time mapping and spatial analysis.

The ongoing development of OGC standards, spatial data infrastructure, and mapping APIs continues to expand the capabilities of GIS, making it an indispensable tool for managing and analyzing geospatial data. The continuous unfolding of market activities and evolving patterns in the market reflect the dynamic nature of this technology and its applications.

How is this GIS Industry segmented?

The GIS industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments.

Product

  Software
  Data
  Services


Type

  Telematics and navigation
  Mapping
  Surveying
  Location-based services


Device

  Desktop
  Mobile


Geography

  North America

    US
    Canada


  Europe

    France
    Germany
    UK


  Middle East and Africa

    UAE


  APAC

    China
    Japan
    South Korea


  South America

    Brazil


  Rest of World (ROW)

By Product Insights

The software segment is estimated to witness significant growth during the forecast period.

The Global Geographic Information System (GIS) market encompasses a range of applications and technologies, including raster data, urban planning, geospatial data, geocoding APIs, GIS services, routing APIs, aerial photography, satellite imagery, GIS software, geospatial analytics, public safety, field data collection, transportation planning, precision agriculture, OGC standards, location intelligence, remote sensing, asset management, network analysis, spatial analysis, infrastructure management, spatial data standards, disaster management, environmental monitoring, spatial modeling, coordinate systems, spatial overlay, real-time mapping, mapping APIs, spatial join, mapping applications, smart cities, spatial data infrastructure, map projections, spatial databases, natural resource management, Bing Maps, terrain analysis, vector data, Lidar data, and geographic information systems.

The software segment includes desktop, mobile, cloud, and server solutions. Open-source GIS software, with its industry-specific offerings, poses a challenge to the market, while the adoption of cloud-based GIS software represents an emerging trend. However, the lack of standardization and interoperability issues hinder the widespread adoption of cloud-based solutions. Applications in sectors like public safety, transportation planning, and precision agriculture are driving market growth. Additionally, advancements in technologies like remote sensing, spatial modeling, and real-time mapping are expanding the market's scope.

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The Software segment was valued at USD 5.06 billion in 2019 and sho

Overview

This dataset contains all DOMI Street Closure Permit data in the Computronix (CX) system from the date of its adoption (in May 2020) until the present. The data in each record can be used to determine when street closures are occurring, who is requesting these closures, why the closure is being requested, and for mapping the closures themselves. It is updated hourly (as of March 2024).

Preprocessing/Formatting

It is important to distinguish between a permit, a permit's street closure(s), and the roadway segments that are referenced to that closure(s).

• The CX system identifies a street in segments of roadway. (As an example, the CX system could divide Maple Street into multiple segments.)

• A single street closure may span multiple segments of a street.

• The street closure permit refers to all the component line segments.

• A permit may have multiple streets which are closed. Street closure permits often reference many segments of roadway.

The roadway_id field is a unique GIS line segment representing the aforementioned segments of road. The roadway_id values are assigned internally by the CX system and are unlikely to be known by the permit applicant. A section of roadway may have multiple permits issued over its lifespan. Therefore, a given roadway_id value may appear in multiple permits.

The field closure_id represents a unique ID for each closure, and permit_id uniquely identifies each permit. This is in contrast to the aforementioned roadway_id field which, again, is a unique ID only for the roadway segments.

City teams that use this data requested that each segment of each street closure permit be represented as a unique row in the dataset. Thus, a street closure permit that refers to three segments of roadway would be represented as three rows in the table. Aside from the roadway_id field, most other data from that permit pertains equally to those three rows. Thus, the values in most fields of the three records are identical.

Each row has the fields segment_num and total_segments which detail the relationship of each record, and its corresponding permit, according to street segment. The above example produced three records for a single permit. In this case, total_segments would equal 3 for each record. Each of those records would have a unique value between 1 and 3.

The geometry field consists of string values of lat/long coordinates, which can be used to map the street segments.

All string text (most fields) were converted to UPPERCASE data. Most of the data are manually entered and often contain non-uniform formatting. While several solutions for cleaning the data exist, text were transformed to UPPERCASE to provide some degree of regularization. Beyond that, it is recommended that the user carefully think through cleaning any unstructured data, as there are many nuances to consider. Future improvements to this ETL pipeline may approach this problem with a more sophisticated technique.

Known Uses

These data are used by DOMI to track the status of street closures (and associated permits).

Further Documentation and Resources

An archived dataset containing historical street closure records (from before May of 2020) for the City of Pittsburgh may be found here: https://data.wprdc.org/dataset/right-of-way-permits

Publication Date: April 2025. This data represents Federal properties in New York State derived from a combination of the USGS National Boundary Dataset (NBD) with NYS Publicly Available Parcel data. USGS GU_Reserve feature class "...include extents of forest, grassland, park, wilderness, wildlife, and other reserve areas useful for recreational activities, such as hiking and backpacking. Boundaries data are acquired from a variety of government sources. The data represents the source data with minimal editing or review by USGS." More information and detailed metadata is available here: https://data.usgs.gov/datacatalog/data/USGS:6dcde538-1684-48a0-a8d6-cb671ca0a43e. NYS ITS Geospatial Services publicly available parcel data selection of [OWNER_TYPE] field, where 1 = Federal Classification is based solely on the parcel owner name indicating that the property is owned by the United States. Parcel data that is not publicly available is not included. More information and detailed metadata is available here: https://gis.ny.gov/parcels.These two datasets were combined with a minimum of available common attributes, indicating the Name, Owner, and Address of the property where applicable and/or available. Unique identifiers were retained to link records back to the original datasets. Work to improve and expand upon this Federal properties GIS dataset is on-going. Please contact NYS ITS Geospatial Services at nysgis@its.ny.gov if you have any questions.

Discover the booming Geographic Information System (GIS) Services market! Explore its $15 Billion (2025 est.) size, 8% CAGR, key drivers, trends, and leading companies. Learn about regional market share and future growth projections in this in-depth analysis.

Geographic Information System (GIS) Software Market Outlook

The global Geographic Information System (GIS) software market size is projected to grow from USD 9.1 billion in 2023 to USD 18.5 billion by 2032, reflecting a compound annual growth rate (CAGR) of 8.5% over the forecast period. This growth is driven by the increasing application of GIS software across various sectors such as agriculture, construction, transportation, and utilities, along with the rising demand for location-based services and advanced mapping solutions.

One of the primary growth factors for the GIS software market is the widespread adoption of spatial data by various industries to enhance operational efficiency. In agriculture, for instance, GIS software plays a crucial role in precision farming by aiding in crop monitoring, soil analysis, and resource management, thereby optimizing yield and reducing costs. In the construction sector, GIS software is utilized for site selection, design and planning, and infrastructure management, making project execution more efficient and cost-effective.

Additionally, the integration of GIS with emerging technologies such as Artificial Intelligence (AI) and the Internet of Things (IoT) is significantly enhancing the capabilities of GIS software. AI-driven data analytics and IoT-enabled sensors provide real-time data, which, when combined with spatial data, results in more accurate and actionable insights. This integration is particularly beneficial in fields like smart city planning, disaster management, and environmental monitoring, further propelling the market growth.

Another significant factor contributing to the market expansion is the increasing government initiatives and investments aimed at improving geospatial infrastructure. Governments worldwide are recognizing the importance of GIS in policy-making, urban planning, and public safety, leading to substantial investments in GIS technologies. For example, the U.S. governmentÂ’s Geospatial Data Act emphasizes the development of a cohesive national geospatial policy, which in turn is expected to create more opportunities for GIS software providers.

Geographic Information System Analytics is becoming increasingly pivotal in transforming raw geospatial data into actionable insights. By employing sophisticated analytical tools, GIS Analytics allows organizations to visualize complex spatial relationships and patterns, enhancing decision-making processes across various sectors. For instance, in urban planning, GIS Analytics can identify optimal locations for new infrastructure projects by analyzing population density, traffic patterns, and environmental constraints. Similarly, in the utility sector, it aids in asset management by predicting maintenance needs and optimizing resource allocation. The ability to integrate GIS Analytics with other data sources, such as demographic and economic data, further amplifies its utility, making it an indispensable tool for strategic planning and operational efficiency.

Regionally, North America holds the largest share of the GIS software market, driven by technological advancements and high adoption rates across various sectors. Europe follows closely, with significant growth attributed to the increasing use of GIS in environmental monitoring and urban planning. The Asia Pacific region is anticipated to witness the highest growth rate during the forecast period, fueled by rapid urbanization, infrastructure development, and government initiatives in countries like China and India.

Component Analysis

The GIS software market is segmented into software and services, each playing a vital role in meeting the diverse needs of end-users. The software segment encompasses various types of GIS software, including desktop GIS, web GIS, and mobile GIS. Desktop GIS remains the most widely used, offering comprehensive tools for spatial analysis, data management, and visualization. Web GIS, on the other hand, is gaining traction due to its accessibility and ease of use, allowing users to access GIS capabilities through a web browser without the need for extensive software installations.

Mobile GIS is another crucial aspect of the software segment, providing field-based solutions for data collection, asset management, and real-time decision making. With the increasing use of smartphones and tablets, mobile GIS applications are becoming indispensable for sectors such as utilities, transportation, and

Utility GIS Market Outlook

According to our latest research, the global Utility GIS market size reached USD 2.65 billion in 2024, and is projected to grow at a robust CAGR of 10.2% during the forecast period, reaching an estimated USD 6.23 billion by 2033. The market’s expansion is primarily fueled by the increasing need for efficient infrastructure management, the proliferation of smart grid initiatives, and the growing adoption of digital mapping technologies across electric, water, gas, and telecommunication utilities. As per our latest research, the surge in infrastructure modernization projects globally and the integration of advanced geospatial analytics into utility operations are key factors propelling this market forward.

One of the principal growth drivers of the Utility GIS market is the escalating demand for real-time asset and network management within utility sectors. As utilities face mounting pressure to optimize resource allocation and reduce operational costs, Geographic Information Systems (GIS) have become indispensable tools for visualizing, analyzing, and managing spatial data. Utilities are leveraging GIS platforms to monitor asset health, track outages, and streamline maintenance activities, which enhances service reliability and minimizes downtime. The ability of GIS to integrate with other enterprise systems, such as SCADA and ERP, further amplifies its value proposition, driving widespread adoption across both developed and emerging markets.

Another significant factor contributing to market growth is the global trend towards smart grid and infrastructure modernization. Governments and private sector entities are investing heavily in digital solutions that support sustainable urban development and resilient utility networks. GIS technologies play a crucial role in planning, monitoring, and optimizing smart grids by providing real-time geospatial intelligence. This enables utilities to improve disaster response, forecast demand, and manage distributed energy resources more effectively. Furthermore, the integration of GIS with IoT devices and cloud computing is enabling more scalable and flexible solutions, which is particularly attractive for utilities looking to future-proof their operations.

The rising focus on regulatory compliance and environmental sustainability is also catalyzing the adoption of Utility GIS solutions. Regulatory bodies worldwide are mandating stricter reporting and transparency standards for utility operations, especially in areas related to environmental impact and resource usage. GIS platforms enable utilities to track compliance metrics, monitor environmental risks, and generate detailed reports with spatial context. This not only helps utilities meet regulatory requirements but also supports their sustainability goals by identifying areas for efficiency improvements and resource conservation. The growing emphasis on reducing carbon footprints and enhancing water and energy conservation further underscores the strategic importance of GIS in the utility sector.

Regionally, North America continues to dominate the Utility GIS market, owing to its advanced utility infrastructure, high adoption of smart technologies, and supportive regulatory frameworks. However, Asia Pacific is emerging as the fastest-growing region, driven by rapid urbanization, significant investments in utility modernization, and increasing government initiatives to improve infrastructure resilience. Europe is also witnessing steady growth due to stringent environmental regulations and the ongoing transition towards renewable energy sources. Latin America and the Middle East & Africa are gradually catching up, supported by infrastructure development projects and the need for efficient utility management in resource-constrained environments.

Component Analysis

The Component segment of the Utility GIS market is broadly categorized into Software, Services, and Hardware. Software remains the largest contributor to market reve

MMD uses a Geographic Information System (GIS) to locate and track its mining activities in the state. This is a computer system that can capture, store, analyze and display geographically referenced (location) information. The power of this system is its ability to draw conclusions about relationships between data that have a spatial component. GIS provides a method of displaying accurate mapping and database information to the staff and public.

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.

• 12 GIS-layers of plots in Gothenburg, from 1960 to 2015, in 5-year intervals. Only built upon plots (plots with buildings) are included. 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.

USGS is assessing the feasibility of map projections and grid systems for lunar surface operations. We propose developing a new Lunar Transverse Mercator (LTM), the Lunar Polar Stereographic (LPS), and the Lunar Grid Reference Systems (LGRS). We have also designed additional grids designed to NASA requirements for astronaut navigation, referred to as LGRS in Artemis Condensed Coordinates (ACC), but this is not released here. LTM, LPS, and LGRS are similar in design and use to the Universal Transverse Mercator (UTM), Universal Polar Stereographic (LPS), and Military Grid Reference System (MGRS), but adhere to NASA requirements. LGRS ACC format is similar in design and structure to historic Army Mapping Service Apollo orthotopophoto charts for navigation. The Lunar Transverse Mercator (LTM) projection system is a globalized set of lunar map projections that divides the Moon into zones to provide a uniform coordinate system for accurate spatial representation. It uses a transverse Mercator projection, which maps the Moon into 45 transverse Mercator strips, each 8°, longitude, wide. These transverse Mercator strips are subdivided at the lunar equator for a total of 90 zones. Forty-five in the northern hemisphere and forty-five in the south. LTM specifies a topocentric, rectangular, coordinate system (easting and northing coordinates) for spatial referencing. This projection is commonly used in GIS and surveying for its ability to represent large areas with high positional accuracy while maintaining consistent scale. The Lunar Polar Stereographic (LPS) projection system contains projection specifications for the Moon’s polar regions. It uses a polar stereographic projection, which maps the polar regions onto an azimuthal plane. The LPS system contains 2 zones, each zone is located at the northern and southern poles and is referred to as the LPS northern or LPS southern zone. LPS, like is equatorial counterpart LTM, specifies a topocentric, rectangular, coordinate system (easting and northing coordinates) for spatial referencing. This projection is commonly used in GIS and surveying for its ability to represent large polar areas with high positional accuracy, while maintaining consistent scale across the map region. LGRS is a globalized grid system for lunar navigation supported by the LTM and LPS projections. LGRS provides an alphanumeric grid coordinate structure for both the LTM and LPS systems. This labeling structure is utilized in a similar manner to MGRS. LGRS defines a global area grid based on latitude and longitude and a 25×25 km grid based on LTM and LPS coordinate values. Two implementations of LGRS are used as polar areas require a LPS projection and equatorial areas a transverse Mercator. We describe the difference in the techniques and methods report associated with this data release. Request McClernan et. al. (in-press) for more information. ACC is a method of simplifying LGRS coordinates and is similar in use to the Army Mapping Service Apollo orthotopophoto charts for navigation. These data will be released at a later date. Two versions of the shape files are provided in this data release, PCRS and Display only. See LTM_LPS_LGRS_Shapefiles.zip file. PCRS are limited to a single zone and are projected in either LTM or LPS with topocentric coordinates formatted in Eastings and Northings. Display only shapefiles are formatted in lunar planetocentric latitude and longitude, a Mercator or Equirectangular projection is best for these grids. A description of each grid is provided below: Equatorial (Display Only) Grids: Lunar Transverse Mercator (LTM) Grids: LTM zone borders for each LTM zone Merged LTM zone borders Lunar Polar Stereographic (LPS) Grids: North LPS zone border South LPS zone border Lunar Grid Reference System (LGRS) Grids: Global Areas for North and South LPS zones Merged Global Areas (8°×8° and 8°×10° extended area) for all LTM zones Merged 25km grid for all LTM zones PCRS Shapefiles:` Lunar Transverse Mercator (LTM) Grids: LTM zone borders for each LTM zone Lunar Polar Stereographic (LPS) Grids: North LPS zone border South LPS zone border Lunar Grid Reference System (LGRS) Grids: Global Areas for North and South LPS zones 25km Gird for North and South LPS zones Global Areas (8°×8° and 8°×10° extended area) for each LTM zone 25km grid for each LTM zone The rasters in this data release detail the linear distortions associated with the LTM and LPS system projections. For these products, we utilize the same definitions of distortion as the U.S. State Plane Coordinate System. Scale Factor, k - The scale factor is a ratio that communicates the difference in distances when measured on a map and the distance reported on the reference surface. Symbolically this is the ratio between the maps grid distance and distance on the lunar reference sphere. This value can be precisely calculated and is provided in their defining publication. See Snyder (1987) for derivation of the LPS scale factor. This scale factor is unitless and typically increases from the central scale factor k_0, a projection-defining parameter. For each LPS projection. Request McClernan et. al., (in-press) for more information. Scale Error, (k-1) - Scale-Error, is simply the scale factor differenced from 1. Is a unitless positive or negative value from 0 that is used to express the scale factor’s impact on position values on a map. Distance on the reference surface are expended when (k-1) is positive and contracted when (k-1) is negative. Height Factor, h_F - The Height Factor is used to correct for the difference in distance caused between the lunar surface curvature expressed at different elevations. It is expressed as a ratio between the radius of the lunar reference sphere and elevations measured from the center of the reference sphere. For this work, we utilized a radial distance of 1,737,400 m as recommended by the IAU working group of Rotational Elements (Archinal et. al., 2008). For this calculation, height factor values were derived from a LOLA DEM 118 m v1, Digital Elevation Model (LOLA Science Team, 2021). Combined Factor, C_F – The combined factor is utilized to “Scale-To-Ground” and is used to adjust the distance expressed on the map surface and convert to the position on the actual ground surface. This value is the product of the map scale factor and the height factor, ensuring the positioning measurements can be correctly placed on a map and on the ground. The combined factor is similar to linear distortion in that it is evaluated at the ground, but, as discussed in the next section, differs numerically. Often C_F is scrutinized for map projection optimization. Linear distortion, δ - In keeping with the design definitions of SPCS2022 (Dennis 2023), we refer to scale error when discussing the lunar reference sphere and linear distortion, δ, when discussing the topographic surface. Linear distortion is calculated using C_F simply by subtracting 1. Distances are expended on the topographic surface when δ is positive and compressed when δ is negative. The relevant files associated with the expressed LTM distortion are as follows. The scale factor for the 90 LTM projections: LUNAR_LTM_GLOBAL_PLOT_HEMISPHERES_distortion_K_grid_scale_factor.tif Height Factor for the LTM portion of the Moon: LUNAR_LTM_GLOBAL_PLOT_HEMISPHERES_distortion_EF_elevation_factor.tif Combined Factor in LTM portion of the Moon LUNAR_LTM_GLOBAL_PLOT_HEMISPHERES_distortion_CF_combined_factor.tif The relevant files associated with the expressed LPS distortion are as follows. Lunar North Pole The scale factor for the northern LPS zone: LUNAR_LGRS_NP_PLOT_LPS_K_grid_scale_factor.tif Height Factor for the north pole of the Moon: LUNAR_LGRS_NP_PLOT_LPS_EF_elevation_factor.tif Combined Factor for northern LPS zone: LUNAR_LGRS_NP_PLOT_LPS_CF_combined_factor.tif Lunar South Pole Scale factor for the northern LPS zone: LUNAR_LGRS_SP_PLOT_LPS_K_grid_scale_factor.tif Height Factor for the south pole of the Moon: LUNAR_LGRS_SP_PLOT_LPS_EF_elevation_factor.tif Combined Factor for northern LPS zone: LUNAR_LGRS_SP_PLOT_LPS_CF_combined_factor.tif For GIS utilization of grid shapefiles projected in Lunar Latitude and Longitude, referred to as “Display Only”, please utilize a registered lunar geographic coordinate system (GCS) such as IAU_2015:30100 or ESRI:104903. LTM, LPS, and LGRS PCRS shapefiles utilize either a custom transverse Mercator or polar Stereographic projection. For PCRS grids the LTM and LPS projections are recommended for all LTM, LPS, and LGRS grid sizes. See McClernan et. al. (in-press) for such projections. Raster data was calculated using planetocentric latitude and longitude. A LTM and LPS projection or a registered lunar GCS may be utilized to display this data. Note: All data, shapefiles and rasters, require a specific projection and datum. The projection is recommended as LTM and LPS or, when needed, IAU_2015:30100 or ESRI:104903. The datum utilized must be the Jet Propulsion Laboratory (JPL) Development Ephemeris (DE) 421 in the Mean Earth (ME) Principal Axis Orientation as recommended by the International Astronomy Union (IAU) (Archinal et. al., 2008).

A Subwatershed represents the area where precipitation naturally drains to a common water feature. Subwatersheds are part of a larger system of drainage areas within our larger watersheds like the Cuyahoga, Rocky, and Chargin Rivers. In turn, those watersheds are a part of a larger "basin". For Cuyahoga County and much of its surrounding area, our subwatersheds and watersheds drain into Lake Erie and the Great Lakes Basin.

Each of the small subwatersheds has information about its "parent" watershed group and associated websites, which provide detailed profiles of conditions and issues in the subwatershed.

One key characteristic of watershed health is the portion of its land area that is "impervious", such as roadway or roofs. For each subwatershed, we've indicated its impervious cover percentage. The Center for Watershed Protection provides guidelines on appropriate practices for watersheds based on their impervious cover. For example, highly urbanized areas (highly impervious) may only benefit from limited practices, such as retrofitting stormwater systems or replacing traditional parking surfaces with "pervious" surfaces. Less urbanized areas (less impervious) might benefit more by preserving headwater drainage and wetlands.

See the layer "Local Subwatersheds, By Percent Imperviousness" and the accompanying report from the Center for Watershed Protection: \dpsterfps01.ad.cuyahoga.cc\GIS\GIS DATA\Planning Commission\Greenprint\Documents\CenterForWatershedProtection\ELC_USRM1v2trs.pdf

This is a web map for PDD GIS application. For City of Phoenix internal use only.

A Feature web service of the Address Point file of buildings and properties in New York State. Please note that, due to the large size, the NYS Address Point statewide layer cannot be downloaded in shapefile format. A map service of the Street and Address Maintenance (SAM) Program Address Point file is available here: https://gisservices.its.ny.gov/arcgis/rest/services.SAM Address Points Data Dictionary: https://gis.ny.gov/system/files/documents/2024/02/address-points-data-dictionary.pdf. If the purpose of accessing the address points service is for geocoding, NYS ITS has a publicly available geocoding service which includes the address points along with other layers. For more information about the geocoding service, please visit: https://gis.ny.gov/address-geocoder. For more information about the SAM Program, please visit: https://gis.ny.gov/streets-addresses.Please contact NYS ITS Geospatial Services at nysgis@its.ny.gov if you have any questions. Publication Date: See Update Frequency. Current as of Date: 2 business days prior to Publication Date. Update frequency: Second and fourth Friday of each month. Spatial Reference of Source Data: NAD_1983_UTM_Zone_18N. Spatial Reference of Map Service: WGS 1984 Web Mercator Auxiliary.This feature service is available to the public.