Abstract

This mapping project aimed to create a database of all financial, health, education, and agricultural service access points in Zambia.

Geographic coverage

National coverage except Ikelenge district

Analysis unit

Health care facilities, financial institutions, educational institutions, agricultural service providers

Universe

The project aimed to capture all open and operational touch-points at the time of fieldwork. Active points were considered to have done a transaction in the last 90 days. Not all points are captured due to several factors including:

i) non-location of the points ii) security areas iii) resistance or lack of cooperation iv) dormancy v) safety of fieldwork staff

Kind of data

Census/enumeration data [cen]

Mode of data collection

Face-to-face [f2f]

Research instrument

The questionnaires used in the MSMP are specific to each sector service intermediary. This means that there are 14 different questionnaires, each with its own set of specific questions.

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.

The files linked to this reference are the geospatial data created as part of the completion of the baseline vegetation inventory project for the NPS park unit. Current format is ArcGIS file geodatabase but older formats may exist as shapefiles. Vegetation map development for KNRI has somewhat different protocols than for other Parks. Normally photointerpretation is preceded by extensive field work which includes plot selection and vegetation sampling using detailed descriptions which are subsequently analyzed using ordination and other statistical techniques. The data are then summarized and association descriptions are assigned to each plot or, if the association is previously unrecognized, then a new association name is assigned. Subsequently, the plots locations are compared to its photographic signature and a photointerpretive key is developed. Given the very small size of KNRI and the extensive historical impact and alteration of the vegetation a simplified technique was used. NatureServe developed a list of potential vegetation types prior to any field work. This list was referenced during the field visit and modified after comparison of site characteristics and vegetation descriptions. Aerial photographs were viewed prior to the field visit and areas of like signature were differentiated. All vegetation and land-use information was then transferred to a GIS database using the latest grayscale USGS digital orthophoto quarter-quads as the base map and using a combination of on-screen digitizing and scanning techniques. Overall thematic map accuracy for the Park is considered 100% as all interpreted polygons received a filed visit for verification.

The objective of this research project is to design, develop, and test a pilot collaborative environment between two Irrigation Districts and Reclamation within the Missouri Basin (MB Region). The collaborative environment will utilize ArcGIS Online, ArcGIS Pro, and Field Maps. Through robust testing, the design process, procedural standards, and lessons learned in the implementing stages will be documented and shared with all Regions. This catalog record contains the Final S&T Project Report describing the work done in the project, and two shapefiles with point and line geometry types depicting observation wells and canals obtained from field GPS data collection by Frenchman Cambridge Irrigation District.

Utility GIS Field Data Collection Market Outlook

According to our latest research, the Global Utility GIS Field Data Collection market size was valued at $1.4 billion in 2024 and is projected to reach $3.1 billion by 2033, expanding at a robust CAGR of 9.3% during the forecast period of 2025–2033. The significant growth in this market is primarily driven by the increasing adoption of advanced geospatial technologies by utility companies seeking to modernize their infrastructure and enhance operational efficiency. The proliferation of smart grids, the growing need for real-time asset monitoring, and the integration of IoT devices have collectively intensified the demand for precise, field-based GIS data collection solutions. This market is further propelled by regulatory mandates emphasizing infrastructure resilience and digital transformation initiatives across the utilities sector, making GIS field data collection systems indispensable for asset management, network mapping, and operational optimization.

Regional Outlook

North America holds the largest share of the global Utility GIS Field Data Collection market, accounting for nearly 38% of the total market value in 2024. This dominance is underpinned by the region’s mature utility infrastructure, widespread digitalization, and early adoption of GIS technologies. The United States, in particular, has invested heavily in upgrading aging utility networks and deploying smart grid solutions, which has necessitated sophisticated GIS field data collection tools. Additionally, favorable regulatory frameworks and a strong presence of leading GIS software providers have accelerated technology uptake. The emphasis on disaster management, grid reliability, and environmental compliance further amplifies the demand for advanced GIS field data collection systems in North America.

In contrast, Asia Pacific emerges as the fastest-growing region, projected to register an impressive CAGR of 12.1% over the forecast period. The rapid urbanization, expanding utility networks, and significant government investments in infrastructure modernization across China, India, and Southeast Asia are pivotal growth drivers. These economies are leveraging GIS field data collection to support mega infrastructure projects, rural electrification, and efficient resource management. The increasing penetration of cloud-based GIS solutions and mobile data collection apps is enabling utilities in Asia Pacific to overcome legacy system limitations, optimize field operations, and improve service delivery. As a result, the region is witnessing a surge in both public and private sector investments aimed at digitalizing utility asset management.

Meanwhile, emerging economies in Latin America and Middle East & Africa are gradually adopting Utility GIS Field Data Collection technologies, albeit at a slower pace due to budget constraints, skills shortages, and infrastructural challenges. These regions face unique hurdles such as fragmented utility networks, inconsistent regulatory support, and limited access to advanced geospatial tools. However, localized demand is rising as governments and utility operators recognize the value of GIS in reducing losses, improving maintenance cycles, and supporting sustainable resource management. International aid programs, technology transfer initiatives, and growing awareness of digital transformation benefits are expected to accelerate adoption in these regions over the next decade.

Report Scope

The Digital Bedrock Geologic-GIS Map of Saugus Iron Works National Historic Site, Massachusetts is composed of GIS data layers and GIS tables, and is available in the following GRI-supported GIS data formats: 1.) an ESRI file geodatabase (sair_bedrock_geology.gdb), a 2.) Open Geospatial Consortium (OGC) geopackage, and 3.) 2.2 KMZ/KML file for use in Google Earth, however, this format version of the map is limited in data layers presented and in access to GRI ancillary table information. The file geodatabase format is supported with a 1.) ArcGIS Pro 3.X map file (.mapx) file (sair_bedrock_geology.mapx) and individual Pro 3.X layer (.lyrx) files (for each GIS data layer). The OGC geopackage is supported with a QGIS project (.qgz) file. Upon request, the GIS data is also available in ESRI shapefile format. Contact Stephanie O'Meara (see contact information below) to acquire the GIS data in these GIS data formats. In addition to the GIS data and supporting GIS files, three additional files comprise a GRI digital geologic-GIS dataset or map: 1.) a readme file (sair_geology_gis_readme.pdf), 2.) the GRI ancillary map information document (.pdf) file (sair_geology.pdf) which contains geologic unit descriptions, as well as other ancillary map information and graphics from the source map(s) used by the GRI in the production of the GRI digital geologic-GIS data for the park, and 3.) a user-friendly FAQ PDF version of the metadata (sair_bedrock_geology_metadata_faq.pdf). Please read the sair_geology_gis_readme.pdf for information pertaining to the proper extraction of the GIS data and other map files. Google Earth software is available for free at: https://www.google.com/earth/versions/. QGIS software is available for free at: https://www.qgis.org/en/site/. Users are encouraged to only use the Google Earth data for basic visualization, and to use the GIS data for any type of data analysis or investigation. The data were completed as a component of the Geologic Resources Inventory (GRI) program, a National Park Service (NPS) Inventory and Monitoring (I&M) Division funded program that is administered by the NPS Geologic Resources Division (GRD). For a complete listing of GRI products visit the GRI publications webpage: https://www.nps.gov/subjects/geology/geologic-resources-inventory-products.htm. For more information about the Geologic Resources Inventory Program visit the GRI webpage: https://www.nps.gov/subjects/geology/gri.htm. At the bottom of that webpage is a "Contact Us" link if you need additional information. You may also directly contact the program coordinator, Jason Kenworthy (jason_kenworthy@nps.gov). Source geologic maps and data used to complete this GRI digital dataset were provided by the following: Massachusetts Geological Survey and U.S. Geological Survey. Detailed information concerning the sources used and their contribution the GRI product are listed in the Source Citation section(s) of this metadata record (sair_bedrock_geology_metadata.txt or sair_bedrock_geology_metadata_faq.pdf). Users of this data are cautioned about the locational accuracy of features within this dataset. Based on the source map scale of 1:24,000 and United States National Map Accuracy Standards features are within (horizontally) 12.2 meters or 40 feet of their actual location as presented by this dataset. Users of this data should thus not assume the location of features is exactly where they are portrayed in Google Earth, ArcGIS Pro, QGIS or other software used to display this dataset. All GIS and ancillary tables were produced as per the NPS GRI Geology-GIS Geodatabase Data Model v. 2.3. (available at: https://www.nps.gov/articles/gri-geodatabase-model.htm).

GIS-material for the archaeological project: Settlement and field by Toketorp

The ZIP file consist of GIS files and an Access database with information about the excavations, findings and other metadata about the archaeological survey.

The information in the abstract is translated from the archeological report: During autumn 2004 and spring 2005 an archaeological research investigation was performed on the Omberg hill in western Östergötland, with the purpose of understanding the prehistoric settlement and farming. The subject of examination was the archaeological site RAÄ 188 in Västra Tollstad parish, Ödeshög municipality. The site is a large, continuous area of abandoned fields, with clearances, clearance cairns, several hollow ways and one registered grave. In October 2004 a phosphate analysis was made of part of the area. In June 2005 four test pits were excavated in four different parts of the site. Two of them were placed in possible settlement terraces, that turned out to be natural, and the other two were dug in clearance cairns. Nothing speaks against the assumption of extensive farming with recurrent periods of fallow and stones being added to the cairns every time the land was reclaimed.

Purpose:

The information in the purpose is translated from the archaeological report: The aim of the excavation was to get an understanding of the prehistoric settlement and agriculture. Where is the settlement located in relation to the mapped abandoned fields? Can the clearance cairns and settlements be dated? The purpose of the geological part of the field work was to examine the stratigraphy and pedology of the clearance cairns. The purpose of the macroscopic analysis was to examine how the aggregated seeds in the cairns differ from the current flora. The results were meant to form the basis of a reconstruction of the area's environmental history.

The ZIP file consist of GIS files and an Access database with information about the excavations, findings and other metadata about the archaeological survey.

The construction of this data model was adapted from the Telvent Miner & Miner ArcFM MultiSpeak data model to provide interface functionality with Milsoft Utility Solutions WindMil engineering analysis program. Database adaptations, GPS data collection, and all subsequent GIS processes were performed by Southern Geospatial Services for the Town of Apex Electric Utilities Division in accordance to the agreement set forth in the document "Town of Apex Electric Utilities GIS/GPS Project Proposal" dated March 10, 2008. Southern Geospatial Services disclaims all warranties with respect to data contained herein. Questions regarding data quality and accuracy should be directed to persons knowledgeable with the forementioned agreement.The data in this GIS with creation dates between March of 2008 and April of 2024 were generated by Southern Geospatial Services, PLLC (SGS). The original inventory was performed under the above detailed agreement with the Town of Apex (TOA). Following the original inventory, SGS performed maintenance projects to incorporate infrastructure expansion and modification into the GIS via annual service agreements with TOA. These maintenances continued through April of 2024.At the request of TOA, TOA initiated in house maintenance of the GIS following delivery of the final SGS maintenance project in April of 2024. GIS data created or modified after April of 2024 are not the product of SGS.With respect to SGS generated GIS data that are point features:GPS data collected after January 1, 2013 were surveyed using mapping grade or survey grade GPS equipment with real time differential correction undertaken via the NC Geodetic Surveys Real Time Network (VRS). GPS data collected prior to January 1, 2013 were surveyed using mapping grade GPS equipment without the use of VRS, with differential correction performed via post processing.With respect to SGS generated GIS data that are line features:Line data in the GIS for overhead conductors were digitized as straight lines between surveyed poles. Line data in the GIS for underground conductors were digitized between surveyed at grade electric utility equipment. The configurations and positions of the underground conductors are based on TOA provided plans. The underground conductors are diagrammatic and cannot be relied upon for the determination of the actual physical locations of underground conductors in the field.The Service Locations feature class was created by Southern Geospatial Services (SGS) from a shapefile of customer service locations generated by dataVoice International (DV) as part of their agreement with the Town of Apex (TOA) regarding the development and implemention of an Outage Management System (OMS).Point features in this feature class represent service locations (consumers of TOA electric services) by uniquely identifying the features with the same unique identifier as generated for a given service location in the TOA Customer Information System (CIS). This is also the mechanism by which the features are tied to the OMS. Features are physically located in the GIS based on CIS address in comparison to address information found in Wake County GIS property data (parcel data). Features are tied to the GIS electric connectivity model by identifying the parent feature (Upline Element) as the transformer that feeds a given service location.SGS was provided a shapefile of 17992 features from DV. Error potentially exists in this DV generated data for the service location features in terms of their assigned physical location, phase, and parent element.Regarding the physical location of the features, SGS had no part in physically locating the 17992 features as provided by DV and cannot ascertain the accuracy of the locations of the features without undertaking an analysis designed to verify or correct for error if it exists. SGS constructed the feature class and loaded the shapefile objects into the feature class and thus the features exist in the DV derived location. SGS understands that DV situated the features based on the address as found in the CIS. No features were verified as to the accuracy of their physical location when the data were originally loaded. It is the assumption of SGS that the locations of the vast majority of the service location features as provided by DV are in fact correct.SGS understands that as a general rule that DV situated residential features (individually or grouped) in the center of a parcel. SGS understands that for areas where multiple features may exist in a given parcel (such as commercial properties and mobile home parks) that DV situated features as either grouped in the center of the parcel or situated over buildings, structures, or other features identifiable in air photos. It appears that some features are also grouped in roads or other non addressed locations, likely near areas where they should physically be located, but that these features were not located in a final manner and are either grouped or strung out in a row in the general area of where DV may have expected they should exist.Regarding the parent and phase of the features, the potential for error is due to the "first order approximation" protocol employed by DV for assigning the attributes. With the features located as detailed above, SGS understands that DV identified the transformer closest to the service location (straight line distance) as its parent. Phase was assigned to the service location feature based on the phase of the parent transformer. SGS expects that this protocol correctly assigned parent (and phase) to a significant portion of the features, however this protocol will also obviously incorretly assign parent in many instances.To accurately identify parent for all 17992 service locations would require a significant GIS and field based project. SGS is willing to undertake a project of this magnitude at the discretion of TOA. In the meantime, SGS is maintaining (editing and adding to) this feature class as part of the ongoing GIS maintenance agreement that is in place between TOA and SGS. In lieu of a project designed to quality assess and correct for the data provided by DV, SGS will verify the locations of the features at the request of TOA via comparison of the unique identifier for a service location to the CIS address and Wake County parcel data address as issues arise with the OMS if SGS is directed to focus on select areas for verification by TOA. Additionally, as SGS adds features to this feature class, if error related to the phase and parent of an adjacent feature is uncovered during a maintenance, it will be corrected for as part of that maintenance.With respect to the additon of features moving forward, TOA will provide SGS with an export of CIS records for each SGS maintenance, SGS will tie new accounts to a physical location based on address, SGS will create a feature for the CIS account record in this feature class at the center of a parcel for a residential address or at the center of a parcel or over the correct (or approximately correct) location as determined via air photos or via TOA plans for commercial or other relevant areas, SGS will identify the parent of the service location as the actual transformer that feeds the service location, and SGS will identify the phase of the service address as the phase of it's parent.Service locations with an ObjectID of 1 through 17992 were originally physically located and attributed by DV.Service locations with an ObjectID of 17993 or higher were originally physically located and attributed by SGS.DV originated data are provided the Creation User attribute of DV, however if SGS has edited or verified any aspect of the feature, this attribute will be changed to SGS and a comment related to the edits will be provided in the SGS Edits Comments data field. SGS originated features will be provided the Creation User attribute of SGS. Reference the SGS Edits Comments attribute field Metadata for further information.

Summary of Field Work 2018. Summary of Field Work and Other Activities presenting highlights of, and key new information from mapping and geoscientific research conducted during the year.Authors: Bedeaux, P., Mathieu, L., and Daigneault, R.

Mobile Work Order Apps with GIS Market Outlook

According to our latest research, the global Mobile Work Order Apps with GIS market size reached USD 2.83 billion in 2024, and the market is projected to expand at a CAGR of 16.2% from 2025 to 2033, reaching a forecasted value of USD 10.16 billion by 2033. The robust growth of this market is primarily driven by the surge in demand for real-time asset tracking, optimized field workforce management, and the increasing adoption of Geographic Information System (GIS) technologies across diverse industrial verticals. These factors are enabling organizations to improve operational efficiency, reduce costs, and deliver enhanced customer service through seamless integration of location intelligence with mobile work order management.

One of the key growth factors propelling the Mobile Work Order Apps with GIS market is the expanding need for digital transformation in asset-intensive sectors such as utilities, oil and gas, and transportation. Companies in these sectors are under immense pressure to streamline their operations, minimize downtime, and ensure regulatory compliance. Mobile work order apps, when combined with GIS capabilities, offer powerful tools for field workforce automation, asset lifecycle management, and predictive maintenance. These solutions facilitate real-time data capture, mapping, and visualization, enabling field technicians to access critical information on-the-go and make informed decisions quickly. As a result, organizations are increasingly investing in these integrated platforms to enhance productivity, reduce manual errors, and optimize resource allocation.

Another significant driver for the market is the growing adoption of cloud-based deployment models, which offer scalability, flexibility, and cost-effectiveness. Cloud-based Mobile Work Order Apps with GIS allow organizations to centralize data management, ensure seamless updates, and enable remote access for field teams. This is particularly valuable for enterprises with geographically dispersed assets and a mobile workforce. Furthermore, the proliferation of smartphones and advancements in mobile network connectivity have made it easier for organizations to deploy these solutions at scale. As a result, both large enterprises and small-to-medium businesses are leveraging cloud-based GIS-enabled work order apps to gain a competitive edge and improve service delivery.

The increasing focus on sustainability and infrastructure modernization is also fueling market growth. Governments and public utilities are investing heavily in smart infrastructure projects, which require advanced tools for monitoring, maintenance, and field operations. Mobile Work Order Apps with GIS play a pivotal role in these initiatives by providing real-time location data, facilitating efficient dispatch of field personnel, and supporting proactive maintenance strategies. Additionally, these solutions are instrumental in supporting regulatory compliance, safety protocols, and environmental monitoring, further driving their adoption across sectors such as government, facilities management, and manufacturing.

From a regional perspective, North America continues to dominate the Mobile Work Order Apps with GIS market, accounting for the largest share in 2024. This dominance is attributed to the early adoption of advanced GIS technologies, strong presence of leading solution providers, and substantial investments in digital infrastructure. However, the Asia Pacific region is expected to witness the fastest growth during the forecast period, driven by rapid industrialization, urbanization, and increasing government initiatives for smart city development. Europe also represents a significant market, with growing demand from utilities, transportation, and manufacturing sectors seeking to modernize their field operations and asset management practices.

Component Analysis

The Mobile Work Order Apps with GIS market by component is segmented into software and services. The software segment encompasses a wide range of applications designed to facilitate work order management, asset tracking, and GIS integration. These solutions provide robust functionalities such as real-time mapping, scheduling, route optimization, and analytics. The growing complexity of field operations and the need for seamless integration with enterprise resource planning (ERP) and asset management systems are driving the adoption of comprehensive softw

Replication data for the Measuring Agricultural survey Bias Across Couples with GIS and Lab-in-the-field research project. This research is part of the IPA Research Methods Initiative.

Table 3. Comparison of in-house project personnel expertise to carry out remote sensing, GIS and carbon pool inventorying works at a sample of REDD+ projects in Brazil, Peru, Cameroon, Tanzania, Indonesia and Vietnam. The upper left columns indicate high degree of expertise (70%) within the organizations. Abstract A functional measuring, monitoring, reporting and verification (MRV) system is essential to assess the additionality and impact on forest carbon in REDD+ (reducing emissions from deforestation and degradation) projects. This study assesses the MRV capacity and readiness of project developers at 20 REDD+ projects in Brazil, Peru, Cameroon, Tanzania, Indonesia and Vietnam, using a questionnaire survey and field visits. Nineteen performance criteria with 76 indicators were formulated in three categories, and capacity was measured with respect to each category. Of the 20 projects, 11 were found to have very high or high overall MRV capacity and readiness. At the regional level, capacity and readiness tended to be highest in the projects in Brazil and Peru and somewhat lower in Cameroon, Tanzania, Indonesia and Vietnam. Although the MRV capacities of half the projects are high, there are capacity deficiencies in other projects that are a source of concern. These are not only due to limitations in technical expertise, but can also be attributed to the slowness of international REDD+ policy formulation and the unclear path of development of the forest carbon market. Based on the study results, priorities for MRV development and increased investment in readiness are proposed.

Soil map units are the basic geographic unit of the Soil Survey Geographic Database (SSURGO). The SSURGO dataset is a compilation of soils information collected over the last century by the Natural Resources Conservation Service (NRCS). Map units delineate the extent of different soils. Data for each map unit contains descriptions of the soil’s components, productivity, unique properties, and suitability interpretations.Each soil type has a unique combination of physical, chemical, nutrient and moisture properties. Soil type has ramifications for engineering and construction activities, natural hazards such as landslides, agricultural productivity, the distribution of native plant and animal life and hydrologic and other physical processes. Soil types in the context of climate and terrain can be used as a general indicator of engineering constraints, agriculture suitability, biological productivity and the natural distribution of plants and animals. Data from the gSSURGO databasewas used to create this layer. To download ready-to-use project packages of useful soil data derived from the SSURGO dataset, please visit the USA SSURGO Downloader app. Dataset SummaryPhenomenon Mapped: Soils of the United States and associated territoriesGeographic Extent: The 50 United States, Puerto Rico, Guam, US Virgin Islands, Marshall Islands, Northern Marianas Islands, Palau, Federated States of Micronesia, and American SamoaCoordinate System: Web Mercator Auxiliary SphereVisible Scale: 1:144,000 to 1:1,000Source: USDA Natural Resources Conservation ServiceUpdate Frequency: AnnualPublication Date: December 2024 What can you do with this layer?ArcGIS OnlineFeature layers work throughout the ArcGIS system. Generally your work flow with feature layers will begin in ArcGIS Online or ArcGIS Pro.Below are just a few of the things you can do with a feature service in Online and Pro.Add this layer to a map in the map viewer. The layer is limited to scales of approximately 1:144,000 or larger but avector tile layercreated from the same data can be used at smaller scales to produce awebmapthat displays across the full scale range. The layer or a map containing it can be used in an application.Change the layer’s transparency and set its visibility rangeOpen the layer’s attribute table and make selections and apply filters. Selections made in the map or table are reflected in the other. Center on selection allows you to zoom to features selected in the map or table and show selected records allows you to view the selected records in the table.Change the layer’s style and filter the data. For example, you could set a filter forFarmland Class= "All areas are prime farmland" to create a map of only prime farmland.Add labels and set their propertiesCustomize the pop-up ArcGIS ProAdd this layer to a 2d or 3d map. The same scale limit as Online applies in ProUse as an input to geoprocessing. For example, copy features allows you to select then export portions of the data to a new feature class.Change the symbology and the attribute field used to symbolize the dataOpen table and make interactive selections with the mapModify the pop-upsApply Definition Queries to create sub-sets of the layerThis layer is part of theLiving Atlas of the Worldthat provides an easy way to explore the landscape layers and many other beautiful and authoritative maps on hundreds of topics. Data DictionaryAttributesKey fields from nine commonly used SSURGO tables were compiled to create the 173 attribute fields in this layer. Some fields were joined directly to the SSURGO Map Unit polygon feature class while others required summarization and other processing to create a 1:1 relationship between the attributes and polygons prior to joining the tables. Attributes of this layer are listed below in their order of occurrence in the attribute table and are organized by the SSURGO table they originated from and the processing methods used on them. Map Unit Polygon Feature Class Attribute TableThe fields in this table are from the attribute table of the Map Unit polygon feature class which provides the geographic extent of the map units.Area SymbolSpatial VersionMap Unit Symbol Map Unit TableThe fields in this table have a 1:1 relationship with the map unit polygons and were joined to the table using the Map Unit Key field.Map Unit NameMap Unit KindFarmland ClassInterpretive FocusIntensity of MappingIowa Corn Suitability Rating Legend TableThis table has 1:1 relationship with the Map Unit table and was joined using the Legend Key field.Project Scale Survey Area Catalog TableThe fields in this table have a 1:1 relationship with the polygons and were joined to the Map Unit table using the Survey Area Catalog Key and Legend Key fields.Survey Area VersionTabular Version Map Unit Aggregated Attribute TableThe fields in this table have a 1:1 relationship with the map unit polygons and were joined to the Map Unit attribute table using the Map Unit Key field. Slope Gradient - Dominant ComponentSlope Gradient - Weighted AverageBedrock Depth - MinimumWater Table Depth - Annual MinimumWater Table Depth - April to June MinimumFlooding Frequency - Dominant ConditionFlooding Frequency - MaximumPonding Frequency - PresenceAvailable Water Storage 0-25 cm - Weighted AverageAvailable Water Storage 0-50 cm - Weighted AverageAvailable Water Storage 0-100 cm - Weighted AverageAvailable Water Storage 0-150 cm - Weighted AverageDrainage Class - Dominant ConditionDrainage Class - WettestHydrologic Group - Dominant ConditionIrrigated Capability Class - Dominant ConditionIrrigated Capability Class - Proportion of Mapunit with Dominant ConditionNon-Irrigated Capability Class - Dominant ConditionNon-Irrigated Capability Class - Proportion of Mapunit with Dominant ConditionRating for Buildings without Basements - Dominant ConditionRating for Buildings with Basements - Dominant ConditionRating for Buildings with Basements - Least LimitingRating for Buildings with Basements - Most LimitingRating for Septic Tank Absorption Fields - Dominant ConditionRating for Septic Tank Absorption Fields - Least LimitingRating for Septic Tank Absorption Fields - Most LimitingRating for Sewage Lagoons - Dominant ConditionRating for Sewage Lagoons - Dominant ComponentRating for Roads and Streets - Dominant ConditionRating for Sand Source - Dominant ConditionRating for Sand Source - Most ProbableRating for Paths and Trails - Dominant ConditionRating for Paths and Trails - Weighted AverageErosion Hazard of Forest Roads and Trails - Dominant ComponentHydric Classification - PresenceRating for Manure and Food Processing Waste - Weighted Average Component Table – Dominant ComponentMap units have one or more components. To create a 1:1 join component data must be summarized by map unit. For these fields a custom script was used to select the component with the highest value for the Component Percentage Representative Value field (comppct_r). Ties were broken with the Slope Representative Value field (slope_r). Components with lower average slope were selected as dominant. If both soil order and slope were tied, the first value in the table was selected. Component Percentage - Low ValueComponent Percentage - Representative ValueComponent Percentage - High ValueComponent NameComponent KindOther Criteria Used to Identify ComponentsCriteria Used to Identify Components at the Local LevelRunoff ClassSoil loss tolerance factorWind Erodibility IndexWind Erodibility GroupErosion ClassEarth Cover 1Earth Cover 2Hydric ConditionHydric RatingAspect Range - Counter Clockwise LimitAspect - Representative ValueAspect Range - Clockwise LimitGeomorphic DescriptionNon-Irrigated Capability SubclassNon-Irrigated Unit Capability ClassIrrigated Capability SubclassIrrigated Unit Capability ClassConservation Tree Shrub GroupGrain Wildlife HabitatGrass Wildlife HabitatHerbaceous Wildlife HabitatShrub Wildlife HabitatConifer Wildlife HabitatHardwood Wildlife HabitatWetland Wildlife HabitatShallow Water Wildlife HabitatRangeland Wildlife HabitatOpenland Wildlife HabitatWoodland Wildlife HabitatWetland Wildlife HabitatSoil Slip PotentialSusceptibility to Frost HeavingConcrete CorrosionSteel CorrosionTaxonomic ClassTaxonomic OrderTaxonomic SuborderGreat GroupSubgroupParticle SizeParticle Size ModCation Exchange Activity ClassCarbonate ReactionTemperature ClassMoist SubclassSoil Temperature RegimeEdition of Keys to Soil Taxonomy Used to Classify SoilCalifornia Storie IndexComponent Key Component Table – Weighted AverageMap units may have one or more soil components. To create a 1:1 join, data from the Component table must be summarized by map unit. For these fields a custom script was used to calculate an average value for each map unit weighted by the Component Percentage Representative Value field (comppct_r).Slope Gradient - Low ValueSlope Gradient - Representative ValueSlope Gradient - High ValueSlope Length USLE - Low ValueSlope Length USLE - Representative ValueSlope Length USLE - High ValueElevation - Low ValueElevation - Representative ValueElevation - High ValueAlbedo - Low ValueAlbedo - Representative ValueAlbedo - High ValueMean Annual Air Temperature - Low ValueMean Annual Air Temperature - Representative ValueMean Annual Air Temperature - High ValueMean Annual Precipitation - Low ValueMean Annual Precipitation - Representative ValueMean Annual Precipitation - High ValueRelative Effective Annual Precipitation - Low ValueRelative Effective Annual Precipitation - Representative ValueRelative Effective Annual Precipitation - High ValueDays between Last and First Frost - Low ValueDays between Last and First Frost - Representative ValueDays between Last and First Frost - High ValueRange Forage Annual Potential Production - Low ValueRange Forage Annual Potential Production - Representative ValueRange Forage Annual Potential Production - High ValueInitial Subsidence - Low ValueInitial Subsidence - Representative ValueInitial Subsidence -

The Digital Geologic-GIS Map of the Mud Creek Quadrangle, Colorado is composed of GIS data layers and GIS tables, and is available in the following GRI-supported GIS data formats: 1.) an ESRI file geodatabase (mucr_geology.gdb), a 2.) Open Geospatial Consortium (OGC) geopackage, and 3.) 2.2 KMZ/KML file for use in Google Earth, however, this format version of the map is limited in data layers presented and in access to GRI ancillary table information. The file geodatabase format is supported with a 1.) ArcGIS Pro map file (.mapx) file (mucr_geology.mapx) and individual Pro layer (.lyrx) files (for each GIS data layer). The OGC geopackage is supported with a QGIS project (.qgz) file. Upon request, the GIS data is also available in ESRI shapefile format. Contact Stephanie O'Meara (see contact information below) to acquire the GIS data in these GIS data formats. In addition to the GIS data and supporting GIS files, three additional files comprise a GRI digital geologic-GIS dataset or map: 1.) a readme file (yuho_geology_gis_readme.pdf), 2.) the GRI ancillary map information document (.pdf) file (yuho_geology.pdf) which contains geologic unit descriptions, as well as other ancillary map information and graphics from the source map(s) used by the GRI in the production of the GRI digital geologic-GIS data for the park, and 3.) a user-friendly FAQ PDF version of the metadata (mucr_geology_metadata_faq.pdf). Please read the yuho_geology_gis_readme.pdf for information pertaining to the proper extraction of the GIS data and other map files. Google Earth software is available for free at: https://www.google.com/earth/versions/. QGIS software is available for free at: https://www.qgis.org/en/site/. Users are encouraged to only use the Google Earth data for basic visualization, and to use the GIS data for any type of data analysis or investigation. The data were completed as a component of the Geologic Resources Inventory (GRI) program, a National Park Service (NPS) Inventory and Monitoring (I&M) Division funded program that is administered by the NPS Geologic Resources Division (GRD). For a complete listing of GRI products visit the GRI publications webpage: https://www.nps.gov/subjects/geology/geologic-resources-inventory-products.htm. For more information about the Geologic Resources Inventory Program visit the GRI webpage: https://www.nps.gov/subjects/geology/gri.htm. At the bottom of that webpage is a "Contact Us" link if you need additional information. You may also directly contact the program coordinator, Jason Kenworthy (jason_kenworthy@nps.gov). Source geologic maps and data used to complete this GRI digital dataset were provided by the following: U.S. Geological Survey. Detailed information concerning the sources used and their contribution the GRI product are listed in the Source Citation section(s) of this metadata record (mucr_geology_metadata.txt or mucr_geology_metadata_faq.pdf). Users of this data are cautioned about the locational accuracy of features within this dataset. Based on the source map scale of 1:24,000 and United States National Map Accuracy Standards features are within (horizontally) 12.2 meters or 40 feet of their actual location as presented by this dataset. Users of this data should thus not assume the location of features is exactly where they are portrayed in Google Earth, ArcGIS Pro, QGIS or other software used to display this dataset. All GIS and ancillary tables were produced as per the NPS GRI Geology-GIS Geodatabase Data Model v. 2.3. (available at: https://www.nps.gov/articles/gri-geodatabase-model.htm).

The East Bay Regional Park District (EBRPD) initiated this project to map the topography, physical and biotic features, and diverse plant communities of the east bay region. This project was funded by the California Department of Forestry and Fire Protection (CAL FIRE), the California State Coastal Conservancy (SCC), and California Department of Fish and Wildlife (CDFW) grants. The mapping study area, consists of approximately 987,000 acres of Alameda and Contra Costa counties. This 115-class fine scale vegetation map was completed in May 2025 and contains 140,442 polygons. The map is based on summer 2020 National Aerial Imagery Program (NAIP) imagery. The map additionally contains lidar-derived information about stand height, canopy cover, and percentage of impervious cover as well as canopy mortality data for each polygon. The minimum mapping unit (MMU) for this project ranges from 1/5 to 1 acre depending on feature type, and is described in detail in the mapping report (Tukman Geospatial, 2025). Development of the Alameda and Contra Costa fine scale vegetation map was managed by EBRPD and staffed by personnel from Tukman Geospatial. Field surveys were completed by trained botanists from the California Native Plant Society (CNPS), who were assisted by botanists from Nomad Ecology Consulting. Data from these surveys, combined with older surveys from previous efforts, were analyzed by the CNPS Vegetation Program, with support from the CDFW Vegetation Classification and Mapping Program (VegCAMP) to develop a county-specific vegetation classification. The floristic classification follows protocols compliant with the Federal Geographic Data Committee (FGDC) and National Vegetation Classification Standards (NVCS). For more information on the field sampling and vegetation classification work, refer to the final report issued by CNPS and corresponding floristic descriptions (Sikes et al., 2025), which are bundled with the vegetation map published for BIOS here: https://filelib.wildlife.ca.gov/Public/BDB/GIS/BIOS/Public_Datasets/3200_3299/ds3206.zipThe foundation for this vegetation map is an enhanced lifeform map produced in 2023 with funding from CAL FIRE. This lifeform map was developed using fine scale segmentation in Trimble® Ecognition® with machine learning and further manual image interpretation. In 2023-2025, Tukman Geospatial and Nomad Ecology staff conducted countywide reconnaissance field work. Field-collected data was used to train automated machine learning algorithms, which produced a semi-automated countywide fine scale vegetation and habitat map. Throughout 2024 and 2025, Tukman Geospatial manually edited the fine scale maps, and Tukman Geospatial and Nomad Ecology went to the field for validation trips to inform and improve the manual editing process. In 2025, input from Alameda and Contra Costa counties’ community of land managers and by the funders of the project was used to further refine the map.Accuracy assessment plot data were collected in 2025. Accuracy assessment results were compiled and analyzed May of 2025. The overall accuracy of the vegetation map by lifeform is 97%. The overall accuracy of the vegetation map by fine scale vegetation map class is 80.8%, with an overall ‘fuzzy’ accuracy of 93.1%.For a complete datasheet of the product, click here. Map class definitions, as well as a dichotomous key for the map classes, can be found in the Alameda and Contra Costa Fine Scale Vegetation Map Key (https://vegmap.press/alcc_mapping_key). A key to map class abbreviations is also available (https://vegmap.press/alcc_vegmap_abbrevs).

The Digital Surficial Geologic-GIS Map of Weir Farm National Historical Park and Vicinity, Connecticut is composed of GIS data layers and GIS tables, and is available in the following GRI-supported GIS data formats: 1.) a 10.1 file geodatabase (wefa_surficial_geology.gdb), a 2.) Open Geospatial Consortium (OGC) geopackage, and 3.) 2.2 KMZ/KML file for use in Google Earth, however, this format version of the map is limited in data layers presented and in access to GRI ancillary table information. The file geodatabase format is supported with a 1.) ArcGIS Pro map file (.mapx) file (wefa_surficial_geology.mapx) and individual Pro layer (.lyrx) files (for each GIS data layer), as well as with a 2.) 10.1 ArcMap (.mxd) map document (wefa_surficial_geology.mxd) and individual 10.1 layer (.lyr) files (for each GIS data layer). The OGC geopackage is supported with a QGIS project (.qgz) file. Upon request, the GIS data is also available in ESRI 10.1 shapefile format. Contact Stephanie O'Meara (see contact information below) to acquire the GIS data in these GIS data formats. In addition to the GIS data and supporting GIS files, three additional files comprise a GRI digital geologic-GIS dataset or map: 1.) A GIS readme file (wefa_geology_gis_readme.pdf), 2.) the GRI ancillary map information document (.pdf) file (wefa_geology.pdf) which contains geologic unit descriptions, as well as other ancillary map information and graphics from the source map(s) used by the GRI in the production of the GRI digital geologic-GIS data for the park, and 3.) a user-friendly FAQ PDF version of the metadata (wefa_surficial_geology_metadata_faq.pdf). Please read the wefa_geology_gis_readme.pdf for information pertaining to the proper extraction of the GIS data and other map files. Google Earth software is available for free at: https://www.google.com/earth/versions/. QGIS software is available for free at: https://www.qgis.org/en/site/. Users are encouraged to only use the Google Earth data for basic visualization, and to use the GIS data for any type of data analysis or investigation. The data were completed as a component of the Geologic Resources Inventory (GRI) program, a National Park Service (NPS) Inventory and Monitoring (I&M) Division funded program that is administered by the NPS Geologic Resources Division (GRD). For a complete listing of GRI products visit the GRI publications webpage: For a complete listing of GRI products visit the GRI publications webpage: https://www.nps.gov/subjects/geology/geologic-resources-inventory-products.htm. For more information about the Geologic Resources Inventory Program visit the GRI webpage: https://www.nps.gov/subjects/geology/gri,htm. At the bottom of that webpage is a "Contact Us" link if you need additional information. You may also directly contact the program coordinator, Jason Kenworthy (jason_kenworthy@nps.gov). Source geologic maps and data used to complete this GRI digital dataset were provided by the following: U.S. Geological Survey. Detailed information concerning the sources used and their contribution the GRI product are listed in the Source Citation section(s) of this metadata record (wefa_surficial_geology_metadata.txt or wefa_surficial_geology_metadata_faq.pdf). Users of this data are cautioned about the locational accuracy of features within this dataset. Based on the source map scale of 1:24,000 and United States National Map Accuracy Standards features are within (horizontally) 12.2 meters or 40 feet of their actual location as presented by this dataset. Users of this data should thus not assume the location of features is exactly where they are portrayed in Google Earth, ArcGIS, QGIS or other software used to display this dataset. All GIS and ancillary tables were produced as per the NPS GRI Geology-GIS Geodatabase Data Model v. 2.3. (available at: https://www.nps.gov/articles/gri-geodatabase-model.htm).

Data Background:This layer displays the general areas of capital projects along with associated project data. It is maintained in accordance with section 10.22.160 of the Tacoma Municipal Code: "The Public Works Department may develop a capital projects layer on its GIS mapping system, entitled “Capital Improvement Projects,” where it will identify its capital improvement projects. Once established, all public and private Tacoma Municipal Code (Revised 4/2018) 10-44 City Clerk’s Office utilities and operators of any communications or cable system shall identify and update their capital projects on the Capital Improvement Projects map, in accordance with Local Law. The Public Works Department, all utilities, and all communications or cable system operators are responsible for updating their capital improvement projects on no less than a calendar quarterly basis."Public Works project data is updated monthly by project managers. Recommended Symbology:"cipstatus" field valuePolygon FillHex/TransparencyPolygon OutlineHex/Transparency/WidthDrawing OrderYes#0078BD/50%#0078BD/0%/2px SolidTopNoNo Fill/100%#999999/50%/1.5px DashedBottomSome projects do not have mappable work areas because they involve work throughout the city or have otherwise indeterminate work areas. For dataset integrity purposes, these projects are mapped as a polygon encompassing the city limits of Tacoma and given a value of "No" in the field "cipstatus". Selecting individual features is difficult if these features are not hidden, transparent, or drawn first. To improve functionality while viewing mapped features, the above symbology and drawing order is recommended. Depending on your use case, you might also simply choose to filter out features with a "cipstatus" value of "No".Unique Fields: projname Official project title used in documentation

websiteurl URL for the project's individual web page (if it has one)

project_type Primary type of asset involved

project_description Overview of project scope

project_rationale Description of justification for the work

current_phase Capital projects typically progress through some or all of the following phases in order:Unfunded: Bringing a construction idea to life requires funds. Projects marked as "Unfunded" are in the process of securing funding and approval. They are not considered active yet.Planning: The project has confirmed some or all funding, and a plan needs to be made to get it moving. The Planning phase involves gathering people and resources to map out the project's future.Design: If not already fully funded by this point, the project has at least enough funding to be completely designed. An engineering team decides how the work should be done and what the final result must include.Right-of-Way (ROW): At this stage, the project team secures the project area for construction. They find potential legal issues and solve them with things like securing permits, making negotiations, or notifying property owners/businesses.Ad-Award: Project plans are advertised so potential contractors can bid on performing the work. The City awards the project contract based on cost estimates and guidelines such as equity in contracting.Construction: The project is fully funded. The City's construction team and any contractors collaborate to perform and inspect the work.Closeout: After construction is substantially complete, documentation and finances are squared away.Complete: All processes to perform the work have been completed. The project is no longer active.Work might also be paused during any phase due to unforeseen issues. This marks the project phase as On Hold.

phase_notes Brief progress update to elaborate on the current phase

construction_start Month and Year in which construction is estimated to start. Projects in early phases may not have this estimate ready.

construction_end Month and Year in which construction is estimated to be completed. Projects in early phases may not have this estimate ready.

citywide Some projects do not have precise mapped locations and are given the value "citywide". This is most often because the project is actually an ongoing project fund that continuously affects many locations every year (example: Unfit/Unsafe Sidewalk Program) or because the project's goal is to conduct a study to determine future work locations.

business_districts City of Tacoma Business Districts containing any of the project area

city_council_districts City Council Districts containing any of the project area

neighborhood_councils City of Tacoma Neighborhood Councils containing any of the project area

total_estimated_cost Estimated combined cost of the project throughout its lifetime in dollars. Might be blank or very rough estimate for early-stage projects

confirmed_funds_so_far Dollar amount that has been secured toward the total cost of the project

associated_programs_6ytip "Yes" if the project is in the 6-Year Transportation Improvement Plan

associated_programs_cfp "Yes" if the project is in the Capital Facilities Plan

associated_programs_si "Yes" if the project is associated with the Tacoma Streets Initiative

lead_department Department/organization with primary ownership of the project

partners Other departments/organizations/entities that support the project, financially or otherwise

contact_name Subject Matter Expert of the project

contact_email Subject Matter Expert's email address to contact with questions about the project

contact_phone Subject Matter Expert's phone number to contact with questions about the project

cipstatus "Yes" if the precise project area is mapped; "No" if the project area is indeterminate and mapped as a city boundary polygon This is a layer view. The original dataset contains many non-viewer-friendly fields structured for HTML and Arcade functionality in various apps, maps, websites, and reports such as Capital Project Highlights, Capital Improvement Plan web app, Capital Facilities Plan documentation, and more. Omitted fields can be seen in the App View of this dataset.Data Owner:Natasha MillerAssociate Civil Engineer -- Asset Managementnmiller@cityoftacoma.org

Beginning with the discovery of a "curious valley" in 1903 by Captain Scott, the McMurdo Dry Valleys (MDV) in Antarctica have been impacted by humans, although there were only three brief visits prior to 1950. Since the late 1950's, human activity in the MDV has become commonplace in summer, putting pressure on the region's fragile ecosystems through camp construction and inhabitation, cross-valley transport on foot and via vehicles, and scientific research that involves sampling and deployment of instruments. Historical photographs, put alongside information from written documentation, offer an invaluable record of the changing patterns of human activity in the MDV. Photographic images often show the physical extent of field camps and research sites, the activities that were taking place, and the environmental protection measures that were being followed. Historical photographs of the MDV, however, are scattered in different places around the world, often in private collections, and there is a real danger that many of these photos may be lost, along with the information they contain. This project will collect and digitize historical photographs of sites of human activity in the MDV from archives and private collections in the United States, New Zealand, and organize them both chronologically and spatially in a GIS database. Sites of past human activities will be re-photographed to provide comparisons with the present, and re-photography will assist in providing spatial data for historical photographs without obvious location information. The results of this analysis will support effective environmental management into the future. The digital photo archive will be openly available through the McMurdo Dry Valleys Long Term Ecological Research (MCM LTER) website (www.mcmlter.org), where it can be used by scientists, environmental managers, and others interested in the region.

The central question of this project can be reformulated as a hypothesis: Despite an overall increase in human activities in the MDV, the spatial range of these activities has become more confined over time as a result of an increased awareness of ecosystem fragility and efforts to manage the region. To address this hypothesis, the project will define the spatial distribution and temporal frequency of human activity in the MDV. Photographs and reports will be collected from archives with polar collections such as the National Archives of New Zealand in Wellington and Christchurch and the Byrd Polar Research Center in Ohio. Private photograph collections will be accessed through personal connections, social media, advertisements in periodicals such as The Polar Times, and other means. Re-photography in the field will follow established techniques and will create benchmarks for future research projects. The spatial data will be stored in an ArcGIS database for analysis and quantification of the human footprint over time in the MDV. The improved understanding of changing patterns of human activity in the MDV provided by this historical photo archive will provide three major contributions: 1) a fundamentally important historic accounting of human activity to support current environmental management of the MDV; 2) defining the location and type of human activity will be of immediate benefit in two important ways: a) places to avoid for scientists interested in sampling pristine landscapes, and, b) targets of opportunity for scientists investigating the long-term environmental legacy of human activity; and 3) this research will make an innovative contribution to knowledge of the environmental history of the MDV.

The purpose of the Colorado Geological Survey’s (CGS) Antero Reservoir Quadrangle Geologic Map, Park and Chaffee Counties, Colorado is to describe the geology, mineral and ground‐water resources, and geologic hazards of this 7.5‐minute quadrangle located in central Colorado. Consulting geologists Robert Kirkham and Karen Houck, CGS staff geologist Chris Carroll, and field assistant Alyssa Heberton‐Morimoto completed field work for the project during the 2007 field season. Mr. Kirkham, Dr. Houck, and Mr. Carroll, the principal mappers and authors, created this report using field maps, photographs, structural measurements, and field notes generated by all four investigators.