Dataset for the textbook Computational Methods and GIS Applications in Social Science (3rd Edition), 2023 Fahui Wang, Lingbo Liu Main Book Citation: Wang, F., & Liu, L. (2023). Computational Methods and GIS Applications in Social Science (3rd ed.). CRC Press. https://doi.org/10.1201/9781003292302 KNIME Lab Manual Citation: Liu, L., & Wang, F. (2023). Computational Methods and GIS Applications in Social Science - Lab Manual. CRC Press. https://doi.org/10.1201/9781003304357 KNIME Hub Dataset and Workflow for Computational Methods and GIS Applications in Social Science-Lab Manual Update Log If Python package not found in Package Management, use ArcGIS Pro's Python Command Prompt to install them, e.g., conda install -c conda-forge python-igraph leidenalg NetworkCommDetPro in CMGIS-V3-Tools was updated on July 10,2024 Add spatial adjacency table into Florida on June 29,2024 The dataset and tool for ABM Crime Simulation were updated on August 3, 2023, The toolkits in CMGIS-V3-Tools was updated on August 3rd,2023. Report Issues on GitHub https://github.com/UrbanGISer/Computational-Methods-and-GIS-Applications-in-Social-Science Following the website of Fahui Wang : http://faculty.lsu.edu/fahui Contents Chapter 1. Getting Started with ArcGIS: Data Management and Basic Spatial Analysis Tools Case Study 1: Mapping and Analyzing Population Density Pattern in Baton Rouge, Louisiana Chapter 2. Measuring Distance and Travel Time and Analyzing Distance Decay Behavior Case Study 2A: Estimating Drive Time and Transit Time in Baton Rouge, Louisiana Case Study 2B: Analyzing Distance Decay Behavior for Hospitalization in Florida Chapter 3. Spatial Smoothing and Spatial Interpolation Case Study 3A: Mapping Place Names in Guangxi, China Case Study 3B: Area-Based Interpolations of Population in Baton Rouge, Louisiana Case Study 3C: Detecting Spatiotemporal Crime Hotspots in Baton Rouge, Louisiana Chapter 4. Delineating Functional Regions and Applications in Health Geography Case Study 4A: Defining Service Areas of Acute Hospitals in Baton Rouge, Louisiana Case Study 4B: Automated Delineation of Hospital Service Areas in Florida Chapter 5. GIS-Based Measures of Spatial Accessibility and Application in Examining Healthcare Disparity Case Study 5: Measuring Accessibility of Primary Care Physicians in Baton Rouge Chapter 6. Function Fittings by Regressions and Application in Analyzing Urban Density Patterns Case Study 6: Analyzing Population Density Patterns in Chicago Urban Area >Chapter 7. Principal Components, Factor and Cluster Analyses and Application in Social Area Analysis Case Study 7: Social Area Analysis in Beijing Chapter 8. Spatial Statistics and Applications in Cultural and Crime Geography Case Study 8A: Spatial Distribution and Clusters of Place Names in Yunnan, China Case Study 8B: Detecting Colocation Between Crime Incidents and Facilities Case Study 8C: Spatial Cluster and Regression Analyses of Homicide Patterns in Chicago Chapter 9. Regionalization Methods and Application in Analysis of Cancer Data Case Study 9: Constructing Geographical Areas for Mapping Cancer Rates in Louisiana Chapter 10. System of Linear Equations and Application of Garin-Lowry in Simulating Urban Population and Employment Patterns Case Study 10: Simulating Population and Service Employment Distributions in a Hypothetical City Chapter 11. Linear and Quadratic Programming and Applications in Examining Wasteful Commuting and Allocating Healthcare Providers Case Study 11A: Measuring Wasteful Commuting in Columbus, Ohio Case Study 11B: Location-Allocation Analysis of Hospitals in Rural China Chapter 12. Monte Carlo Method and Applications in Urban Population and Traffic Simulations Case Study 12A. Examining Zonal Effect on Urban Population Density Functions in Chicago by Monte Carlo Simulation Case Study 12B: Monte Carlo-Based Traffic Simulation in Baton Rouge, Louisiana Chapter 13. Agent-Based Model and Application in Crime Simulation Case Study 13: Agent-Based Crime Simulation in Baton Rouge, Louisiana Chapter 14. Spatiotemporal Big Data Analytics and Application in Urban Studies Case Study 14A: Exploring Taxi Trajectory in ArcGIS Case Study 14B: Identifying High Traffic Corridors and Destinations in Shanghai Dataset File Structure 1 BatonRouge Census.gdb BR.gdb 2A BatonRouge BR_Road.gdb Hosp_Address.csv TransitNetworkTemplate.xml BR_GTFS Google API Pro.tbx 2B Florida FL_HSA.gdb R_ArcGIS_Tools.tbx (RegressionR) 3A China_GX GX.gdb 3B BatonRouge BR.gdb 3C BatonRouge BRcrime R_ArcGIS_Tools.tbx (STKDE) 4A BatonRouge BRRoad.gdb 4B Florida FL_HSA.gdb HSA Delineation Pro.tbx Huff Model Pro.tbx FLplgnAdjAppend.csv 5 BRMSA BRMSA.gdb Accessibility Pro.tbx 6 Chicago ChiUrArea.gdb R_ArcGIS_Tools.tbx (RegressionR) 7 Beijing BJSA.gdb bjattr.csv R_ArcGIS_Tools.tbx (PCAandFA, BasicClustering) 8A Yunnan YN.gdb R_ArcGIS_Tools.tbx (SaTScanR) 8B Jiangsu JS.gdb 8C Chicago ChiCity.gdb cityattr.csv ...

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

Features of different accessibility analysis methods.

Every four years, the Wasatch Front’s two metropolitan planning organizations (MPOs), Wasatch Front Regional Council (WFRC) and Mountainland Association of Governments (MAG), collaborate to update a set of annual small area -- traffic analysis zone and ‘city area’, see descriptions below) -- population and employment projections for the Salt Lake City-West Valley City (WFRC), Ogden-Layton (WFRC), and Provo-Orem (MAG) urbanized areas.

These projections are primarily developed for the purpose of informing long-range transportation infrastructure and services planning done as part of the 4 year Regional Transportation Plan update cycle, as well as Utah’s Unified Transportation Plan, 2023-2050. Accordingly, the foundation for these projections is largely data describing existing conditions for a 2019 base year, the first year of the latest RTP process. The projections are included in the official travel models, which are publicly released at the conclusion of the RTP process.

Projections within the Wasatch Front urban area ( SUBAREAID = 1) were produced with using the Real Estate Market Model as described below. Socioeconomic forecasts produced for Cache MPO (Cache County, SUBAREAID = 2), Dixie MPO (Washington County, SUBAREAID = 3), Summit County (SUBAREAID = 4), and UDOT (other areas of the state, SUBAREAID = 0) all adhere to the University of Utah Gardner Policy Institute's county-level projection controls, but other modeling methods are used to arrive at the TAZ-level forecasts for these areas.

As these projections may be a valuable input to other analyses, this dataset is made available here as a public service for informational purposes only. It is solely the responsibility of the end user to determine the appropriate use of this dataset for other purposes.

Wasatch Front Real Estate Market Model (REMM) Projections

WFRC and MAG have developed a spatial statistical model using the UrbanSim modeling platform to assist in producing these annual projections. This model is called the Real Estate Market Model, or REMM for short. REMM is used for the urban portion of Weber, Davis, Salt Lake, and Utah counties. REMM relies on extensive inputs to simulate future development activity across the greater urbanized region. Key inputs to REMM include:

Demographic data from the decennial census
County-level population and employment projections -- used as REMM control totals -- are produced by the University of Utah’s Kem C. Gardner Policy Institute (GPI) funded by the Utah State Legislature
Current employment locational patterns derived from the Utah Department of Workforce Services
Land use visioning exercises and feedback, especially in regard to planned urban and local center development, with city and county elected officials and staff
Current land use and valuation GIS-based parcel data stewarded by County Assessors
Traffic patterns and transit service from the regional Travel Demand Model that together form the landscape of regional accessibility to workplaces and other destinations
Calibration of model variables to balance the fit of current conditions and dynamics at the county and regional level

‘Traffic Analysis Zone’ Projections

The annual projections are forecasted for each of the Wasatch Front’s 3,546 Traffic Analysis Zone (TAZ) geographic units. TAZ boundaries are set along roads, streams, and other physical features and average about 600 acres (0.94 square miles). TAZ sizes vary, with some TAZs in the densest areas representing only a single city block (25 acres).

‘City Area’ Projections

The TAZ-level output from the model is also available for ‘city areas’ that sum the projections for the TAZ geographies that roughly align with each city’s current boundary. As TAZs do not align perfectly with current city boundaries, the ‘city area’ summaries are not projections specific to a current or future city boundary, but the ‘city area’ summaries may be suitable surrogates or starting points upon which to base city-specific projections.

Summary Variables in the Datasets

Annual projection counts are available for the following variables (please read Key Exclusions note below):

Demographics

Household Population Count (excludes persons living in group quarters) 
Household Count (excludes group quarters) 

Employment

Typical Job Count (includes job types that exhibit typical commuting and other travel/vehicle use patterns)
Retail Job Count (retail, food service, hotels, etc)
Office Job Count (office, health care, government, education, etc)
Industrial Job Count (manufacturing, wholesale, transport, etc)
Non-Typical Job Count* (includes agriculture, construction, mining, and home-based jobs) This can be calculated by subtracting Typical Job Count from All Employment Count 
All Employment Count* (all jobs, this sums jobs from typical and non-typical sectors).

• These variables includes REMM’s attempt to estimate construction jobs in areas that experience new and re-development activity. Areas may see short-term fluctuations in Non-Typical and All Employment counts due to the temporary location of construction jobs.

Key Exclusions from TAZ and ‘City Area’ Projections

As the primary purpose for the development of these population and employment projections is to model future travel in the region, REMM-based projections do not include population or households that reside in group quarters (prisons, senior centers, dormitories, etc), as residents of these facilities typically have a very low impact on regional travel. USTM-based projections also excludes group quarter populations. Group quarters population estimates are available at the county-level from GPI and at various sub-county geographies from the Census Bureau.

Statewide Projections

Population and employment projections for the Wasatch Front area can be combined with those developed by Dixie MPO (St. George area), Cache MPO (Logan area), and the Utah Department of Transportation (for the remainder of the state) into one database for use in the Utah Statewide Travel Model (USTM). While projections for the areas outside of the Wasatch Front use different forecasting methods, they contain the same summary-level population and employment projections making similar TAZ and ‘City Area’ data available statewide. WFRC plans, in the near future, to add additional areas to these projections datasets by including the projections from the USTM model.

Fayette County Ohio GIS Addresses Location Based Response System (LBRS). The information provided is for reference only and subject to independent verification. User assumes all responsibility for its use.https://das.ohio.gov/technology-and-strategy/ogrip/projects/lbrsOHIO'S LOCATION BASED RESPONSE SYSTEMThe Location Based Response System (LBRS) is an initiative of the Ohio Geographically Referenced Information Program (OGRIP). The LBRS establishes partnerships between State and County government for the creation of spatially accurate street centerlines with address ranges and field verified site-specific address locations.Funding to support the development of LBRS compliant systems is available to counties through a Memorandum of Agreement (MOA) that establishes roles and responsibilities for program participation. Participating counties provide project management and QA/QC on road names, addresses, etc to develop data that is compatible with the state's legacy roadway inventory.The Ohio Department of Transportation is the LBRS Program Sponsor, providing technical guidance, support, and QA/QC services. The program is being administered by OGRIP, the state's coordinating body for Geographic Information System (GIS) activities.Through the collaborative efforts of State and Local government the LBRS program is producing highly accurate field verified data that is current, complete, consistent, and accessible. LBRS data is maintained as an Ohio asset by local resources and is provided to the state as part of a coordinated long-term effort by OGRIP to reduce redundant data collection by developing data that meets the needs of several levels of government.The LBRS supports a multi-jurisdictional approach to protecting the health, safety and welfare of the state’s constituents.LBRS FAQsWhat is the LBRS? The LBRS is a County/State partnership that gathers accurate locational information on all roads and addresses in a county. The information is used to save lives and save taxpayer dollars by reducing redundant data collection activities. The information is web-based, and is therefore current for all stakeholders as agencies or local governments gather new information.Who is using LBRS data? 9-1-1 Dispatch/First Responders, County Auditors, County Commissioners and Engineers, Ohio Highway Patrol/MARCS, County Emergency Management Agencies, Ohio Department of Transportation, US Department of Homeland Security, US Census Bureau, Ohio Department of Natural Resources, Ohio Department of Agriculture, Ohio Utilites Protection Service.“Both disaster planning and emergency response efforts will benefit from the LBRS. By participating, counties may reduce redundant mapping projects while ensuring that Ohio’s citizens do not pay for multiple mapping initiatives.” - Shawn Smith, Ohio 911 Coordinator - Public Utilities Commission of Ohio.How is the state share determined per county? Each county that participates is assigned a ceiling amount, based on number of addressable structures and miles of public roads. The state share does not exceed 50% of the county’s cost of gathering data, and never exceeds the pre-determined ceiling.What other funding sources are available to support a county's LBRS development? The Ohio Department of Transportation (ODOT) and the County Engineers Association of Ohio (CEAO) each administer Safety Grant programs with funds that can be applied to LBRS projects. How are LBRS funds distributed? Counties enter into a Memorandum of Agreement with the state to secure funding. Counties may contract with a vendor or collect information on their own, with OIT/OGRIP and ODOT providing technical guidance throughout the process. Monies are deliverable based, as a County provides data that meets the State defined standards for program acceptance, the monies are released to the County.

Snapshot img

GIS In Utility Industry Market Size 2025-2029

The gis in utility industry market size is forecast to increase by USD 3.55 billion, at a CAGR of 19.8% between 2024 and 2029.

The utility industry's growing adoption of Geographic Information Systems (GIS) is driven by the increasing need for efficient and effective infrastructure management. GIS solutions enable utility companies to visualize, analyze, and manage their assets and networks more effectively, leading to improved operational efficiency and customer service. A notable trend in this market is the expanding application of GIS for water management, as utilities seek to optimize water distribution and reduce non-revenue water losses. However, the utility GIS market faces challenges from open-source GIS software, which can offer cost-effective alternatives to proprietary solutions. These open-source options may limit the functionality and support available to users, necessitating careful consideration when choosing a GIS solution. To capitalize on market opportunities and navigate these challenges, utility companies must assess their specific needs and evaluate the trade-offs between cost, functionality, and support when selecting a GIS provider. Effective strategic planning and operational execution will be crucial for success in this dynamic market.

What will be the Size of the GIS In Utility Industry 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.
Request Free SampleThe Global Utilities Industry Market for Geographic Information Systems (GIS) continues to evolve, driven by the increasing demand for advanced data management and analysis solutions. GIS services play a crucial role in utility infrastructure management, enabling asset management, data integration, project management, demand forecasting, data modeling, data analytics, grid modernization, data security, field data capture, outage management, and spatial analysis. These applications are not static but rather continuously unfolding, with new patterns emerging in areas such as energy efficiency, smart grid technologies, renewable energy integration, network optimization, and transmission lines. Spatial statistics, data privacy, geospatial databases, and remote sensing are integral components of this evolving landscape, ensuring the effective management of utility infrastructure. Moreover, the adoption of mobile GIS, infrastructure planning, customer service, asset lifecycle management, metering systems, regulatory compliance, GIS data management, route planning, environmental impact assessment, mapping software, GIS consulting, GIS training, smart metering, workforce management, location intelligence, aerial imagery, construction management, data visualization, operations and maintenance, GIS implementation, and IoT sensors is transforming the industry. The integration of these technologies and services facilitates efficient utility infrastructure management, enhancing network performance, improving customer service, and ensuring regulatory compliance. The ongoing evolution of the utilities industry market for GIS reflects the dynamic nature of the sector, with continuous innovation and adaptation to meet the changing needs of utility providers and consumers.

How is this GIS In Utility Industry Industry segmented?

The gis in utility industry 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. ProductSoftwareDataServicesDeploymentOn-premisesCloudGeographyNorth AmericaUSCanadaEuropeFranceGermanyRussiaMiddle East and AfricaUAEAPACChinaIndiaJapanSouth AmericaBrazilRest of World (ROW).

By Product Insights

The software segment is estimated to witness significant growth during the forecast period.In the utility industry, Geographic Information Systems (GIS) play a pivotal role in optimizing operations and managing infrastructure. Utilities, including electricity, gas, water, and telecommunications providers, utilize GIS software for asset management, infrastructure planning, network performance monitoring, and informed decision-making. The GIS software segment in the utility industry encompasses various solutions, starting with fundamental GIS software that manages and analyzes geographical data. Additionally, utility companies leverage specialized software for field data collection, energy efficiency, smart grid technologies, distribution grid design, renewable energy integration, network optimization, transmission lines, spatial statistics, data privacy, geospatial databases, GIS services, project management, demand forecasting, data modeling, data analytics, grid modernization, data security, field data capture, outage ma

The search for the most appropriate GIS data model to integrate, manipulate and analyse spatio-temporal data raises several research questions about the conceptualisation of geographic spaces. Although there is now a general consensus that many environmental phenomena require field and object conceptualisations to provide a comprehensive GIS representation, there is still a need for better integration of these dual representations of space within a formal spatio-temporal database. The research presented in this paper introduces a hybrid and formal dual data model for the representation of spatio-temporal data. The whole approach has been fully implemented in PostgreSQL and its spatial extension PostGIS, where the SQL language is extended by a series of data type constructions and manipulation functions to support hybrid queries. The potential of the approach is illustrated by an application to underwater geomorphological dynamics oriented towards the monitoring of the evolution of seabed changes. A series of performance and scalability experiments are also reported to demonstrate the computational performance of the model.

California State Lands Commission Offshore Oil Leases in the vicinity of Santa Barbara, Ventura, and Orange County.The polygons in this layer show the position of Offshore Oil Leases as documented by former State Lands Senior Boundary Determination Officer, Cris N. Perez and as reviewed and updated by GIS and Boundary staff.Background: This layer represents active offshore oil and gas agreements in California waters, which are what remain of the more than 60 originally issued. These leases were issued prior to the catastrophic 1969 oil spill from Platform A in federal waters off Santa Barbara County, and some predate the formation of the Commission. Between 2010 and 2014, the bulk of the approximately $300 million generated annually for the state's General Fund from oil and gas agreements was from these offshore leases.In 1921, the Legislature created the first tidelands oil and gas leasing program. Between 1921 and 1929, approximately 100 permits and leases were issued and over 850 wells were drilled in Santa Barbara and Ventura Counties. In 1929, the Legislature prohibited any new leases or permits. In 1933, however, the prohibition was partially lifted in response to an alleged theft of tidelands oil in Huntington Beach. It wasn't until 1938, and again in 1955, that the Legislature would allow new offshore oil and gas leasing. Except for limited circumstances, the Legislature has consistently placed limits on the areas that the Commission may offer for lease and in 1994, placed the entirety of California's coast off-limits to new oil and gas leases. Layer Creation Process:In 1997 Cris N. Perez, Senior Boundary Determination Officer of the Southern California Section of the State Lands Division, prepared a report on the Commission’s Offshore Oil Leases to:A. Show the position of Offshore Oil Leases. B. Produce a hard copy of 1927 NAD Coordinates for each lease. C. Discuss any problems evident after plotting the leases.Below are some of the details Cris included in the report:I have plotted the leases that were supplied to me by the Long Beach Office and computed 1927 NAD California Coordinates for each one. Where the Mean High Tide Line (MHTL) was called for and not described in the deed, I have plotted the California State Lands Commission CB Map Coordinates, from the actual field surveys of the Mean High Water Line and referenced them wherever used. Where the MHTL was called for and not described in the deed and no California State Lands Coordinates were available, I digitized the maps entitled, “Map of the Offshore Ownership Boundary of the State of California Drawn pursuant to the Supplemental Decree of the U.S. Supreme Court in the U.S. V. California, 382 U.S. 448 (1966), Scale 1:10000 Sheets 1-161.” The shore line depicted on these maps is the Mean Lower Low Water (MLLW) Line as shown on the Hydrographic or Topographic Sheets for the coastline. If a better fit is needed, a field survey to position this line will need to be done.The coordinates listed in Cris’ report were retrieved through Optical Character Recognition (OCR) and used to produce GIS polygons using Esri ArcGIS software. Coordinates were checked after the OCR process when producing the polygons in ArcMap to ensure accuracy. Original Coordinate systems (NAD 1927 California State Plane Zones 5 and 6) were used initially, with each zone being reprojected to NAD 83 Teale Albers Meters and merged after the review process.While Cris’ expertise and documentation were relied upon to produce this GIS Layer, certain polygons were reviewed further for any potential updates since Cris’ document and for any unusual geometry. Boundary Determination Officers addressed these issues and plotted leases currently listed as active, but not originally in Cris’ report. On December 24, 2014, the SLA boundary offshore of California was fixed (permanently immobilized) by a decree issued by the U.S. Supreme Court United States v. California, 135 S. Ct. 563 (2014). Offshore leases were clipped so as not to exceed the limits of this fixed boundary. Lease Notes:PRC 1482The “lease area” for this lease is based on the Compensatory Royalty Agreement dated 1-21-1955 as found on the CSLC Insider. The document spells out the distinction between “leased lands” and “state lands”. The leased lands are between two private companies and the agreement only makes a claim to the State’s interest as those lands as identified and surveyed per the map Tract 893, Bk 27 Pg 24. The map shows the State’s interest as being confined to the meanders of three sloughs, one of which is severed from the bay (Anaheim) by a Tideland sale. It should be noted that the actual sovereign tide and or submerged lands for this area is all those historic tide and submerged lands minus and valid tide land sales patents. The three parcels identified were also compared to what the Orange County GIS land records system has for their parcels. Shapefiles were downloaded from that site as well as two centerline monuments for 2 roads covered by the Tract 893. It corresponded well, so their GIS linework was held and clipped or extended to make a parcel.MJF Boundary Determination Officer 12/19/16PRC 3455The “lease area” for this lease is based on the Tract No. 2 Agreement, Long Beach Unit, Wilmington Oil Field, CA dated 4/01/1965 and found on the CSLC insider (also recorded March 12, 1965 in Book M 1799, Page 801).Unit Operating Agreement, Long Beach Unit recorded March 12, 1965 in Book M 1799 page 599.“City’s Portion of the Offshore Area” shall mean the undeveloped portion of the Long Beach tidelands as defined in Section 1(f) of Chapter 138, and includes Tract No. 1”“State’s Portion of the Offshore Area” shall mean that portion of the Alamitos Beach Park Lands, as defined in Chapter 138, included within the Unit Area and includes Tract No. 2.”“Alamitos Beach Park Lands” means those tidelands and submerged lands, whether filled or unfilled, described in that certain Judgment After Remittitur in The People of the State of California v. City of Long Beach, Case No. 683824 in the Superior Court of the State of California for the County of Los Angeles, dated May 8, 1962, and entered on May 15, 1962 in Judgment Book 4481, at Page 76, of the Official Records of the above entitled court”*The description for Tract 2 has an EXCEPTING (statement) “therefrom that portion lying Southerly of the Southerly line of the Boundary of Subsidence Area, as shown on Long Beach Harbor Department {LBHD} Drawing No. D-98. This map could not be found in records nor via a PRA request to the LBHD directly. Some maps were located that show the extents of subsidence in this area being approximately 700 feet waterward of the MHTL as determined by SCC 683824. Although the “EXCEPTING” statement appears to exclude most of what would seem like the offshore area (out to 3 nautical miles from the MHTL which is different than the actual CA offshore boundary measured from MLLW) the 1964, ch 138 grant (pg25) seems to reference the lands lying seaward of that MHTL and ”westerly of the easterly boundary of the undeveloped portion of the Long Beach tidelands, the latter of which is the same boundary (NW) of tract 2. This appears to then indicate that the “EXCEPTING” area is not part of the Lands Granted to City of Long Beach and appears to indicate that this portion might be then the “State’s Portion of the Offshore Area” as referenced in the Grant and the Unit Operating Agreement. Section “f” in the CSLC insider document (pg 9) defines the Contract Lands: means Tract No. 2 as described in Exhibit “A” to the Unit Agreement, and as shown on Exhibit “B” to the Unit Agreement, together with all other lands within the State’s Portion of the Offshore Area.Linework has been plotted in accordance with the methods used to produce this layer, with record lines rotated to those as listed in the descriptions. The main boundaries being the MHTL(north/northeast) that appears to be fixed for most of the area (projected to the city boundary on the east/southeast); 3 nautical miles from said MHTL on the south/southwest; and the prolongation of the NWly line of Block 50 of Alamitos Bay Tract.MJF Boundary Determination Officer 12-27-16PRC 4736The “lease area” for this lease is based on the Oil and Gas Lease and Agreement as found on the CSLC insider and recorded August 17, 1973 in BK 10855 PG 432 Official Records, Orange County. The State’s Mineral Interests are confined to Parcels “B-1” and “B-2” and are referred to as “State Mineral Lands” comprising 70.00 Acres. The lessee each has a right to certain uses including but not limited to usage of utility corridors, 110 foot radius parcels surrounding well-sites and roads. The State also has access to those same roads per this agreement/lease. Those uses are allowed in what are termed “State Lands”-Parcel E and “Leased Lands” which are defined as the “South Bolsa Lease Area”-Parcel C (2 parcels) and “North Bolsa Lease Area”-Parcel D. The “State Lands”-Parcel E are actually 3 parcels, 2 of which are within road right-of-ways. MJF Boundary Determination Officer 12-28-16

Snapshot img

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.
Request Free Sample

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.

Request Free Sample

The Software segment was valued at USD 5.06 billion in 2019

GIS Data Collector Market Outlook

The global GIS Data Collector market size is anticipated to grow from USD 4.5 billion in 2023 to approximately USD 12.3 billion by 2032, at a compound annual growth rate (CAGR) of 11.6%. The growth of this market is largely driven by the increasing adoption of GIS technology across various industries, advances in technology, and the need for effective spatial data management.

An important factor contributing to the growth of the GIS Data Collector market is the rising demand for geospatial information across different sectors such as agriculture, construction, and transportation. The integration of advanced technologies like IoT and AI with GIS systems enables the collection and analysis of real-time data, which is crucial for effective decision-making. The increasing awareness about the benefits of GIS technology and the growing need for efficient land management are also fuelling market growth.

The government sector plays a significant role in the expansion of the GIS Data Collector market. Governments worldwide are investing heavily in GIS technology for urban planning, disaster management, and environmental monitoring. These investments are driven by the need for accurate and timely spatial data to address critical issues such as climate change, urbanization, and resource management. Moreover, regulatory policies mandating the use of GIS technology for infrastructure development and environmental conservation are further propelling market growth.

Another major growth factor in the GIS Data Collector market is the continuous technological advancements in GIS software and hardware. The development of user-friendly and cost-effective GIS solutions has made it easier for organizations to adopt and integrate GIS technology into their operations. Additionally, the proliferation of mobile GIS applications has enabled field data collection in remote areas, thus expanding the scope of GIS technology. The advent of cloud computing has further revolutionized the GIS market by offering scalable and flexible solutions for spatial data management.

Regionally, North America holds the largest share of the GIS Data Collector market, driven by the presence of key market players, advanced technological infrastructure, and high adoption rates of GIS technology across various industries. However, the Asia Pacific region is expected to witness the highest growth rate during the forecast period, primarily due to rapid urbanization, government initiatives promoting GIS adoption, and increasing investments in smart city projects. Other regions such as Europe, Latin America, and the Middle East & Africa are also experiencing significant growth in the GIS Data Collector market, thanks to increasing awareness and adoption of GIS technology.

The role of a GPS Field Controller is becoming increasingly pivotal in the GIS Data Collector market. These devices are essential for ensuring that data collected in the field is accurate and reliable. By providing real-time positioning data, GPS Field Controllers enable precise mapping and spatial analysis, which are critical for applications such as urban planning, agriculture, and transportation. The integration of GPS technology with GIS systems allows for seamless data synchronization and enhances the efficiency of data collection processes. As the demand for real-time spatial data continues to grow, the importance of GPS Field Controllers in the GIS ecosystem is expected to rise, driving further innovations and advancements in this segment.

Component Analysis

The GIS Data Collector market is segmented by component into hardware, software, and services. Each of these components plays a crucial role in the overall functionality and effectiveness of GIS systems. The hardware segment includes devices such as GPS units, laser rangefinders, and mobile GIS devices used for field data collection. The software segment encompasses various GIS applications and platforms used for data analysis, mapping, and visualization. The services segment includes consulting, training, maintenance, and support services provided by GIS vendors and solution providers.

In the hardware segment, the demand for advanced GPS units and mobile GIS devices is increasing, driven by the need for accurate and real-time spatial data collection. These devices are equipped with high-precision sensors and advanced features such as real-time kinematic (RTK) positioning, which enhance

Update information can be found within the layer’s attributes and in a table on the Utah Parcel Data webpage under LIR Parcels.In Spring of 2016, the Land Information Records work group, an informal committee organized by the Governor’s Office of Management and Budget’s State Planning Coordinator, produced recommendations for expanding the sharing of GIS-based parcel information. Participants in the LIR work group included representatives from county, regional, and state government, including the Utah Association of Counties (County Assessors and County Recorders), Wasatch Front Regional Council, Mountainland and Bear River AOGs, Utah League of Cities and Towns, UDOT, DNR, AGRC, the Division of Emergency Management, Blue Stakes, economic developers, and academic researchers. The LIR work group’s recommendations set the stage for voluntary sharing of additional objective/quantitative parcel GIS data, primarily around tax assessment-related information. Specifically the recommendations document establishes objectives, principles (including the role of local and state government), data content items, expected users, and a general process for data aggregation and publishing. An important realization made by the group was that ‘parcel data’ or ‘parcel record’ products have a different meaning to different users and data stewards. The LIR group focused, specifically, on defining a data sharing recommendation around a tax year parcel GIS data product, aligned with the finalization of the property tax roll by County Assessors on May 22nd of each year. The LIR recommendations do not impact the periodic sharing of basic parcel GIS data (boundary, ID, address) from the County Recorders to AGRC per 63F-1-506 (3.b.vi). Both the tax year parcel and the basic parcel GIS layers are designed for general purpose uses, and are not substitutes for researching and obtaining the most current, legal land records information on file in County records. This document, below, proposes a schedule, guidelines, and process for assembling county parcel and assessment data into an annual, statewide tax parcel GIS layer. gis.utah.gov/data/sgid-cadastre/It is hoped that this new expanded parcel GIS layer will be put to immediate use supporting the best possible outcomes in public safety, economic development, transportation, planning, and the provision of public services. Another aim of the work group was to improve the usability of the data, through development of content guidelines and consistent metadata documentation, and the efficiency with which the data sharing is distributed.GIS Layer Boundary Geometry:GIS Format Data Files: Ideally, Tax Year Parcel data should be provided in a shapefile (please include the .shp, .shx, .dbf, .prj, and .xml component files) or file geodatabase format. An empty shapefile and file geodatabase schema are available for download at:At the request of a county, AGRC will provide technical assistance to counties to extract, transform, and load parcel and assessment information into the GIS layer format.Geographic Coverage: Tax year parcel polygons should cover the area of each county for which assessment information is created and digital parcels are available. Full coverage may not be available yet for each county. The county may provide parcels that have been adjusted to remove gaps and overlaps for administrative tax purposes or parcels that retain these expected discrepancies that take their source from the legally described boundary or the process of digital conversion. The diversity of topological approaches will be noted in the metadata.One Tax Parcel Record Per Unique Tax Notice: Some counties produce an annual tax year parcel GIS layer with one parcel polygon per tax notice. In some cases, adjacent parcel polygons that compose a single taxed property must be merged into a single polygon. This is the goal for the statewide layer but may not be possible in all counties. AGRC will provide technical support to counties, where needed, to merge GIS parcel boundaries into the best format to match with the annual assessment information.Standard Coordinate System: Parcels will be loaded into Utah’s statewide coordinate system, Universal Transverse Mercator coordinates (NAD83, Zone 12 North). However, boundaries stored in other industry standard coordinate systems will be accepted if they are both defined within the data file(s) and documented in the metadata (see below).Descriptive Attributes:Database Field/Column Definitions: The table below indicates the field names and definitions for attributes requested for each Tax Parcel Polygon record.FIELD NAME FIELD TYPE LENGTH DESCRIPTION EXAMPLE SHAPE (expected) Geometry n/a The boundary of an individual parcel or merged parcels that corresponds with a single county tax notice ex. polygon boundary in UTM NAD83 Zone 12 N or other industry standard coordinates including state plane systemsCOUNTY_NAME Text 20 - County name including spaces ex. BOX ELDERCOUNTY_ID (expected) Text 2 - County ID Number ex. Beaver = 1, Box Elder = 2, Cache = 3,..., Weber = 29ASSESSOR_SRC (expected) Text 100 - Website URL, will be to County Assessor in most all cases ex. webercounty.org/assessorBOUNDARY_SRC (expected) Text 100 - Website URL, will be to County Recorder in most all cases ex. webercounty.org/recorderDISCLAIMER (added by State) Text 50 - Disclaimer URL ex. gis.utah.gov...CURRENT_ASOF (expected) Date - Parcels current as of date ex. 01/01/2016PARCEL_ID (expected) Text 50 - County designated Unique ID number for individual parcels ex. 15034520070000PARCEL_ADD (expected, where available) Text 100 - Parcel’s street address location. Usually the address at recordation ex. 810 S 900 E #304 (example for a condo)TAXEXEMPT_TYPE (expected) Text 100 - Primary category of granted tax exemption ex. None, Religious, Government, Agriculture, Conservation Easement, Other Open Space, OtherTAX_DISTRICT (expected, where applicable) Text 10 - The coding the county uses to identify a unique combination of property tax levying entities ex. 17ATOTAL_MKT_VALUE (expected) Decimal - Total market value of parcel's land, structures, and other improvements as determined by the Assessor for the most current tax year ex. 332000LAND _MKT_VALUE (expected) Decimal - The market value of the parcel's land as determined by the Assessor for the most current tax year ex. 80600PARCEL_ACRES (expected) Decimal - Parcel size in acres ex. 20.360PROP_CLASS (expected) Text 100 - Residential, Commercial, Industrial, Mixed, Agricultural, Vacant, Open Space, Other ex. ResidentialPRIMARY_RES (expected) Text 1 - Is the property a primary residence(s): Y'(es), 'N'(o), or 'U'(nknown) ex. YHOUSING_CNT (expected, where applicable) Text 10 - Number of housing units, can be single number or range like '5-10' ex. 1SUBDIV_NAME (optional) Text 100 - Subdivision name if applicable ex. Highland Manor SubdivisionBLDG_SQFT (expected, where applicable) Integer - Square footage of primary bldg(s) ex. 2816BLDG_SQFT_INFO (expected, where applicable) Text 100 - Note for how building square footage is counted by the County ex. Only finished above and below grade areas are counted.FLOORS_CNT (expected, where applicable) Decimal - Number of floors as reported in county records ex. 2FLOORS_INFO (expected, where applicable) Text 100 - Note for how floors are counted by the County ex. Only above grade floors are countedBUILT_YR (expected, where applicable) Short - Estimated year of initial construction of primary buildings ex. 1968EFFBUILT_YR (optional, where applicable) Short - The 'effective' year built' of primary buildings that factors in updates after construction ex. 1980CONST_MATERIAL (optional, where applicable) Text 100 - Construction Material Types, Values for this field are expected to vary greatly by county ex. Wood Frame, Brick, etc Contact: Sean Fernandez, Cadastral Manager (email: sfernandez@utah.gov; office phone: 801-209-9359)

Population served by existing sports parks in the study area.

Accessibility levels of sports parks in the study area before and after optimization.

Landforms are large recognizable features such as mountains, hills and plains; they are an important determinant of ecological character, habitat definition and terrain analysis. Landforms are important to the distribution of life in natural systems and are the basis for opportunities in built systems, and therefore landforms play a useful role in all natural science fields of study and planning disciplines.Dataset SummaryPhenomenon Mapped: LandformsUnits: MetersCell Size: 231.91560581932 metersSource Type: ThematicPixel Type: 8-bit unsigned integerData Coordinate System: WGS 1984Mosaic Projection: Web Mercator Auxiliary SphereExtent: GlobalSource: EsriPublication Date: May 2016ArcGIS Server URL: https://landscape7.arcgis.com/arcgis/In February 2017, Esri updated the World Landforms - Improved Hammond Method service with two display functions: Ecological Land Units landform classes and Ecological Facets landform classes. This layer represents Ecological Facets landform classes. You can view the Ecological Land Units landform classes by choosing Image Display, and changing the Renderer. This layer was produced using the Improved Hammond Landform Classification Algorithm produced by Esri in 2016. This algorithm published and described by Karagulle et al. 2017: Modeling global Hammond landform regions from 250-m elevation data in Transactions in GIS.The algorithm, which is based on the most recent work in this area by Morgan, J. & Lesh, A. 2005: Developing Landform Maps Using Esri’s Model Builder., Esri converted Morgan’s model into a Python script and revised it to work on global 250-meter resolution GMTED2010 elevation data. Hammond’s landform classification characterizes regions rather than identifying individual features, thus, this layer contains sixteen classes of landforms:Nearly flat plainsSmooth plains with some local reliefIrregular plains with moderate relief Irregular plains with low hillsScattered moderate hillsScattered high hillsScattered low mountainsScattered high mountainsModerate hillsHigh hills Tablelands with moderate reliefTablelands with considerable reliefTablelands with high relief Tablelands with very high relief Low mountainsHigh mountainsTo produce these classes, Esri staff first projected the 250-meter resolution GMTED elevation data to the World Equidistant Cylindrical coordinate system. Each cell in this dataset was assigned three characteristics: slope based on 3-km neighborhood, relief based on 6 km neighborhood, and profile based on 6-km neighborhood. The last step was to overlay the combination of these three characteristics with areas that are exclusively plains. Slope is the percentage of the 3-km neighborhood occupied by gentle slope. Hammond specified 8% as the threshold for gentle slope. Slope is used to define how flat or steep the terrain is. Slope was classified into one of four classes: Percent of neighborhood over 8% of slopeSlope Classes0 - 20%40021% -50%30051% - 80%200>81% 100Local Relief is the difference between the maximum and minimum elevation within in the 6-km neighborhood. Local relief is used to define terrain how rugged or the complexity of the terrain's texture. Relief was assigned one of six classes:Change in elevationRelief Class ID0 – 30 meters1031 meter – 90 meters2091 meter – 150 meters30151 meter – 300 meters40301 meter – 900 meters50>900 meters60The combination of slope and relief begin to define terrain as mountains, hills and plains. However, the difference between mountains or hills and tablelands cannot be distinguished using only these parameters. Profile is used to determine tableland areas. Profile identifies neighborhoods with upland and lowland areas, and calculates the percent area of gently sloping terrain within those upland and lowland areas. A 6-km circular neighborhood was used to calculate the profile parameter. Upland/lowland is determined by the difference between average local relief and elevation. In the 6-km neighborhood window, if the difference between maximum elevation and cell’s elevation is smaller than half of the local relief it’s an upland. If the difference between maximum elevation and cell’s elevation is larger than half of the local relief it’s a lowland. Profile was assigned one of five classes:Percent of neighborhood over 8% slope in upland or lowland areasProfile ClassLess than 50% gentle slope is in upland or lowland0More than 75% of gentle slope is in lowland150%-75% of gentle slope is in lowland250-75% of gentle slope is in upland3More than 75% of gentle slope is in upland4Early reviewers of the resulting classes noted one confusing outcome, which was that areas were classified as "plains with low mountains", or "plains with hills" were often mostly plains, and the hills or mountains were part of an adjacent set of exclusively identified hills or mountains. To address this areas that are exclusively plains were produced, and used to override these confusing areas. The hills and mountains within those areas were converted to their respective landform class.The combination of slope, relief and profile merged with the areas of plains, can be better understood using the following diagram, which uses the colors in this layer to show which classes are present and what parameter values produced them:What can you do with this layer?This layer is suitable for both visualization and analysis. It can be used in ArcGIS Online in web maps and applications and can be used in ArcGIS Desktop. This layer is part of a larger collection of landscape layers that you can use to perform a wide variety of mapping and analysis tasks.The Living Atlas of the World provides an easy way to explore the landscape layers and many other beautiful and authoritative maps on hundreds of topics.Geonet is a good resource for learning more about landscape layers and the Living Atlas of the World. To get started see the Living Atlas Discussion Group.The Esri Insider Blog provides an introduction to the Ecophysiographic Mapping project.

Flood is the most devastating and prevalent disaster among all-natural disasters. Every year, flood claims hundreds of human lives and causes damage to the worldwide economy and environment. Consequently, the identification of flood-vulnerable areas is important for comprehensive flood risk management. The main objective of this study is to delineate flood-prone areas in the Panjkora River Basin (PRB), eastern Hindu Kush, Pakistan. An initial extensive field survey and interpretation of Landsat-7 and Google Earth images identified 154 flood locations that were inundated in 2010 floods. Of the total, 70% of flood locations were randomly used for building a model and 30% were used for validation of the model. Eight flood parameters including slope, elevation, land use, Normalized Difference Vegetation Index (NDVI), topographic wetness index (TWI), drainage density, and rainfall were used to map the flood-prone areas in the study region. The relative frequency ratio was used to determine the correlation between each class of flood parameter and flood occurrences. All of the factors were resampled into a pixel size of 30×30 m and were reclassified through the natural break method. Finally, a final hazard map was prepared and reclassified into five classes, i.e., very low, low, moderate, high, very high susceptibility. The results of the model were found reliable with area under curve values for success and prediction rate of 82.04% and 84.74%, respectively. The findings of this study can play a key role in flood hazard management in the target region; they can be used by the local disaster management authority, researchers, planners, local government, and line agencies dealing with flood risk management.

This layer is subset of World Ecological Facets Landform Classes Image Layer. Landforms are large recognizable features such as mountains, hills and plains; they are an important determinant of ecological character, habitat definition and terrain analysis. Landforms are important to the distribution of life in natural systems and are the basis for opportunities in built systems, and therefore landforms play a useful role in all natural science fields of study and planning disciplines.Dataset SummaryPhenomenon Mapped: LandformsUnits: MetersCell Size: 231.91560581932 metersSource Type: ThematicPixel Type: 8-bit unsigned integerData Coordinate System: WGS 1984Mosaic Projection: Web Mercator Auxiliary SphereExtent: GlobalSource: EsriPublication Date: May 2016ArcGIS Server URL: https://landscape7.arcgis.com/arcgis/In February 2017, Esri updated the World Landforms - Improved Hammond Method service with two display functions: Ecological Land Units landform classes and Ecological Facets landform classes. This layer represents Ecological Facets landform classes. You can view the Ecological Land Units landform classes by choosing Image Display, and changing the Renderer. This layer was produced using the Improved Hammond Landform Classification Algorithm produced by Esri in 2016. This algorithm published and described by Karagulle et al. 2017: Modeling global Hammond landform regions from 250-m elevation data in Transactions in GIS.The algorithm, which is based on the most recent work in this area by Morgan, J. & Lesh, A. 2005: Developing Landform Maps Using Esri’s Model Builder., Esri converted Morgan’s model into a Python script and revised it to work on global 250-meter resolution GMTED2010 elevation data. Hammond’s landform classification characterizes regions rather than identifying individual features, thus, this layer contains sixteen classes of landforms:Nearly flat plainsSmooth plains with some local reliefIrregular plains with moderate relief Irregular plains with low hillsScattered moderate hillsScattered high hillsScattered low mountainsScattered high mountainsModerate hillsHigh hills Tablelands with moderate reliefTablelands with considerable reliefTablelands with high relief Tablelands with very high relief Low mountainsHigh mountainsTo produce these classes, Esri staff first projected the 250-meter resolution GMTED elevation data to the World Equidistant Cylindrical coordinate system. Each cell in this dataset was assigned three characteristics: slope based on 3-km neighborhood, relief based on 6 km neighborhood, and profile based on 6-km neighborhood. The last step was to overlay the combination of these three characteristics with areas that are exclusively plains. Slope is the percentage of the 3-km neighborhood occupied by gentle slope. Hammond specified 8% as the threshold for gentle slope. Slope is used to define how flat or steep the terrain is. Slope was classified into one of four classes: Percent of neighborhood over 8% of slopeSlope Classes0 - 20%40021% -50%30051% - 80%200>81% 100Local Relief is the difference between the maximum and minimum elevation within in the 6-km neighborhood. Local relief is used to define terrain how rugged or the complexity of the terrain's texture. Relief was assigned one of six classes:Change in elevationRelief Class ID0 – 30 meters1031 meter – 90 meters2091 meter – 150 meters30151 meter – 300 meters40301 meter – 900 meters50>900 meters60The combination of slope and relief begin to define terrain as mountains, hills and plains. However, the difference between mountains or hills and tablelands cannot be distinguished using only these parameters. Profile is used to determine tableland areas. Profile identifies neighborhoods with upland and lowland areas, and calculates the percent area of gently sloping terrain within those upland and lowland areas. A 6-km circular neighborhood was used to calculate the profile parameter. Upland/lowland is determined by the difference between average local relief and elevation. In the 6-km neighborhood window, if the difference between maximum elevation and cell’s elevation is smaller than half of the local relief it’s an upland. If the difference between maximum elevation and cell’s elevation is larger than half of the local relief it’s a lowland. Profile was assigned one of five classes:Percent of neighborhood over 8% slope in upland or lowland areasProfile ClassLess than 50% gentle slope is in upland or lowland0More than 75% of gentle slope is in lowland150%-75% of gentle slope is in lowland250-75% of gentle slope is in upland3More than 75% of gentle slope is in upland4Early reviewers of the resulting classes noted one confusing outcome, which was that areas were classified as "plains with low mountains", or "plains with hills" were often mostly plains, and the hills or mountains were part of an adjacent set of exclusively identified hills or mountains. To address this areas that are exclusively plains were produced, and used to override these confusing areas. The hills and mountains within those areas were converted to their respective landform class.The combination of slope, relief and profile merged with the areas of plains, can be better understood using the following diagram, which uses the colors in this layer to show which classes are present and what parameter values produced them:What can you do with this layer?This layer is suitable for both visualization and analysis. It can be used in ArcGIS Online in web maps and applications and can be used in ArcGIS Desktop. This layer is part of a larger collection of landscape layers that you can use to perform a wide variety of mapping and analysis tasks.The Living Atlas of the World provides an easy way to explore the landscape layers and many other beautiful and authoritative maps on hundreds of topics.Geonet is a good resource for learning more about landscape layers and the Living Atlas of the World. To get started see the Living Atlas Discussion Group.The Esri Insider Blog provides an introduction to the Ecophysiographic Mapping project.

BAARI (Bay Area Aquatic Resource Inventory) was established to meet regional needs for wetlands and stream monitoring. One primary objective was to apply standardized monitoring tools to ensure data comparability and consistent, documented quality. These data help agencies and organizations assess the extent and condition of wetlands in local watersheds and identify and prioritize opportunities for ecological restoration and enhancement in a watershed context.BackgroundBAARI data is part of the Wetland and Riparian Area Monitoring Plan (WRAMP), which consists of a 1-2-3 level monitoring framework. This allows assessment of wetlands at two scales: landscape and highly localized. BAARI is Level 1 of this framework, developed for landscape level analyses of wetland extent, distribution and abundance. Geographic information systems (GIS) and remote sensing are used to map and create inventories of existing wetlands (both modern and historical). These inventories quantify the extent of wetland habitats and projects, and are used for landscape profiles of wetlands at the state, regional, watershed, and local scales. Level 2 entails rapid field assessment of wetland health or ecological condition. In California the California Rapid Assessment Method (CRAM) is a diagnostic tool that two or more trained practitioners can use to assess the condition of a wetland or riparian site in one half day or less using visual indicators in the field. Level 3 monitoring entails intensive sampling of ecological function or specific aspects of wetland condition. These assessments are intensive quantitative measurements of condition, stress, or cause-and-effect relationships. A Level 3 assessment is an in-depth study of a particular attribute of wetland health such as water quality, fish habitat, bird populations, vegetative cover and diversity, or physical processes. Level 3 monitoring can describe the performance of specific ecological functions at the site scale. Information on the WRAMP is available at http://www.mywaterquality.ca.gov/eco_health/wetlands/condition/wramp_toolkit.shtmlMore information on BAARI can be found online at: http://www.sfei.org/BAARI. Description of Updates in Version 2.1Added channels in Santa Clara County based on the Santa Clara Valley Water District's 2004 stream layer, available at http://data-valleywater.opendata.arcgis.com/datasets/e74548aaba0e46918523d62645e283fd_9 . Presence of these channels was confirmed in recent aerial imagery. Streams added include teh Permanente Diversion Channel, Upper Sunnyvale West Channel (The upper portion is also referred to as Moffett Channel, sections of teh Vasona Canal. Page Channel also reclassified from a 'Ditch' (FD) to an 'Engineered Channel (FEC)In the Petaluma River Watershed, another SFEI study mapped a few channels in great detail using a Lidar derived DEM. These channels were incorporated into BAARI. Also, a depressional wetland was reclassified as Unnatural (DOWU)Ran topology on the streams layer and corrected small errors. This included removing small segments >50m long, and correcting some overlaps and self-intersections.Description of Updates in Version 2Local experts provided advice on and reviewed BAARI’s stream, wetland and riparian GIS layers in specific locations to help update the data as conditions in the field changed and/or to increase its accuracy and detail. Improving the accuracy and detail of BAARI improves the base maps for all monitoring and assessment efforts to understand the distribution, amount and ecological condition of Bay Area aquatic resources. The following updates were made to BAARI under this project:The Santa Clara Valley Water District (SCVWD) provided their GIS based stream data for the Guadalupe River Watershed. SFEI’s GIS team incorporated parts of the SCVWD dataset into BAARI to improve the accuracy and detail of the BAARI stream layer with the Guadalupe River watershed. Updates included features such as underground connectors between the upper and lower watershed reaches, and enhanced details of portions of the upper watershed. Watershed Sciences developed six hand-drawn maps showing field verified ditch locations circa 2006. Those data were used in developing the sediment TMDL for the watershed. Watershed sciences provided those maps to SFEI’s GIS team and worked with them to compare the remotely sensed stream and ditch locations in the BAARI data set (based on 2009 NAIP imagery) to her maps and other data (including Google Earth using multiple image dates). Based on this careful comparative process, about 500 linear updates were made to the BAARI stream layer for the Sonoma Creek watershed with the data source attributed to Laurel Collins’ maps. SFEI staff added stream names for the whole BAARI dataset (Bay Area 9 counties, ocean shoreline, and Tomales Bay - 530 different names applied to more than 24000 stream segments).Additional corrections to wetland and stream presence, extent, and classification were appended to the dataset by SFEI staff where inaccuracies were identified based on local expertise of wetlands in the Bay Area.All BAARI updates were reviewed by a second GIS staff member to ensure that they were acceptable and followed BAARI mapping protocols. Description of Attribute Fields in BAARIBaylandsRestProj: restoration project associated with this wetland. This field has not been updated in version 2. Refer to http://www.EcoAtlas.org for current listings of ecological restoration projects. Source_Dat: source(s) from which the wetland polygon was digitized, or otherwise incorporated into BAARIWetlandTyp: Coded classification of the wetland type. Codes are defined on p5-6 of "BAARI Mapping Standards" Organization: organization that digitized the wetland polygonClickCode: simplification of the 'WetlandTyp' attribute used for map database queries in web mapping applications such as http://www.EcoAtlas.org. Refers to the standardized wetland classification system employed by the California Aquatic Resources Inventory (CARI). More information about CARI is available at http://www.sfei.org/it/gis/cari ClickLabel: verbal description of the ClickCode codeLegCode: further simplification of the 'WetlandTyp' attribute used in the legends of web mapping applications such as http://www.EcoAtlas.orgLegLabel: verbal description of the LegCode codeORIG_FID: unusedOpenWater: Open Water (1) or a wetland (0)TidRip: unusedGlobalID: Unique identifier, if one exists StreamsWetlandTyp: Coded classification of the stream type. Codes are defined on p9-10 of "BAARI Mapping Standards" Bayland: Identifies whether the feature falls within the historical extent of the Baylands (1) or not (0)Strahler: Strahler stream order of the stream segment.ClickCode: simplification of the 'WetlandTyp' attribute used for map database queries in web mapping applications such as http://www.EcoAtlas.orgClickLabel: verbal description of the ClickCode codeLegCode: further simplification of the 'WetlandTyp' attribute used in the legends of web mapping applications such as http://www.EcoAtlas.orgLegLabel: verbal description of the LegCode codeGlobalID: Unique identifier, if one exists (e.g. ReachCode attribute in NHD)IDNum: Numerical version of GlobalIDStreamName: the recognized Geographic Names Information System (GNIS) name of the streamSource_Dat: source(s) from which the segment was digitized, or otherwise incorporated into BAARI.Name_Source: source from which 'StreamName' was identifiedFNode: "From Node" used to determine stream flow directionTNode: "To Node" used to determine stream flow directionLegHeader: legend header used in the legends of web mapping applications such as http://www.EcoAtlas.orgTMDL_DATA: "Yes" value indicates TMDL data is available for this stream segment. Contact SFEI for information about integrating TMDL data with BAARIWetlands (Non-tidal)WetlandTyp: Coded classification of the wetland type. Codes are defined on p6-9 of "BAARI Mapping Standards" SourceData: source(s) from which the wetland polygon was digitized, or otherwise incorporated into BAARIOrganization: organization that digitized the wetland polygonClickCode: simplification of the 'WetlandTyp' attribute used for map database queries in web mapping applications such as http://www.EcoAtlas.orgClickLabel: verbal description of the ClickCode codeLegCode: further simplification of the 'WetlandTyp' attribute used in the legends of web mapping applications such as http://www.EcoAtlas.orgLegLabel: verbal description of the LegCode codeGlobalID: Unique identifier, if one exists

The perimeter of all public and private sport/recreation courts, including clay, grass, dirt, and paved surfaces are captured. Each court is delineated separately even if adjacent. Pavement around the recreation court is captured as sidewalk, not recreation court. Courts are defined as:Basketball Court: full outline of the court Tennis Court: full outline of the courtBaseball Field: infield and outfield of a baseball area as one polygon if easily defined in aerial photo. Otherwise infield as the baseball field.Football Field: full outline of the field as best defined in aerial photoSoccer Field: full outline of the field as best defined in aerial photoTrack: outline of track. Any recreation fields within tracks are captured and coded separately.Other Recreation Area: For areas that cannot be easily defined by function, they are captured as other recreation area, with full outline of the field as best defined in aerial photo.

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