The field computer market, valued at $3,807 million in 2025, is projected to experience robust growth, driven by increasing adoption across various sectors. The Compound Annual Growth Rate (CAGR) of 5.9% from 2025 to 2033 indicates a significant expansion, fueled primarily by the rising demand for ruggedized and durable computing devices in demanding environments like construction, agriculture, and logistics. Technological advancements, such as improved processing power, enhanced connectivity (5G, satellite), and integrated sensor technologies, are further bolstering market growth. The integration of advanced features like GPS, GIS mapping, and data analytics capabilities within field computers is transforming workflows and increasing efficiency, leading to higher adoption rates. Key players like Panasonic, Getac, and Trimble are continuously innovating to meet the evolving needs of diverse industries, with a focus on user-friendly interfaces and enhanced data security. The market is segmented based on factors such as device type, operating system, application, and end-user industry. While specific segment breakdowns aren't provided, it's reasonable to assume substantial growth within segments focused on advanced features and specific industry applications, particularly those sectors experiencing digital transformation.
Growth restraints could include the relatively high initial investment cost of specialized field computers compared to standard laptops or tablets, and the potential for technological obsolescence as new devices and software are introduced. However, the long-term benefits of increased productivity and improved data management are likely to outweigh these considerations, leading to continued market expansion over the forecast period. Regional variations in market penetration are expected, with developed regions showing higher initial adoption, followed by growth in emerging economies driven by infrastructure development and increased industrialization.

[Source Data]The Digital Divide Index or DDI ranges in value from 0 to 100, where 100 indicates the highest digital divide. It is composed of two scores, also ranging from 0 to 100: the infrastructure/adoption (INFA) score and the socioeconomic (SE) score.The INFA score groups five variables related to broadband infrastructure and adoption: (1) percentage of total 2020 population without access to fixed broadband of at least 100 Mbps download and 20 Mbps upload as of 2020 based on Ookla Speedtest® open dataset; (2) percent of homes without a computing device (desktops, laptops, smartphones, tablets, etc.); (3) percent of homes with no internet access (have no internet subscription, including cellular data plans or dial-up); (4) median maximum advertised download speeds; and (5) median maximum advertised upload speeds.The SE score groups five variables known to impact technology adoption: (1) percent population ages 65 and over; (2) percent population 25 and over with less than high school; (3) individual poverty rate; (4) percent of noninstitutionalized civilian population with a disability: and (5) a brand new digital inequality or internet income ratio measure (IIR). In other words, these variables indirectly measure adoption since they are potential predictors of lagging technology adoption or reinforcing existing inequalities that also affect adoption.These two scores are combined to calculate the overall DDI score. If a particular county or census tract has a higher INFA score versus a SE score, efforts should be made to improve broadband infrastructure. If on the other hand, a particular geography has a higher SE score versus an INFA score, efforts should be made to increase digital literacy and exposure to the technology’s benefits.The DDI measures primarily physical access/adoption and socioeconomic characteristics that may limit motivation, skills, and usage. Due to data limitations it was designed as a descriptive and pragmatic tool and is not intended to be comprehensive. Rather it should help initiate important discussions among community leaders and residents.

This layer shows Technology Access by Household. Data is from US Census American Community Survey (ACS) 5-year estimates.This layer represents the underlying data for several data visualizations on the Tempe Equity Map.Data visualized as a percent of total households in given census tract.Layer includes:Key demographicsTotal Households % With a Desktop or Laptop Computer% With only a Desktop or Laptop% With a Smartphone% With only a Smartphone% With a Tablet% With only a tablet% With other type of computing device% With other type of computing device only% No computerCurrent Vintage: 2017-2021ACS Table(s): S2801 (Not all lines of this ACS table are available in this feature layer.)Data downloaded from: Census Bureau's API for American Community Survey Date of Census update: Dec 8, 2022Data Preparation: Data table downloaded and joined with Census Tract boundaries that are within or adjacent to the City of Tempe boundaryNational Figures: data.census.gov

This map is designated as Final.Land-Use Data Quality ControlEvery published digital survey is designated as either ‘Final’, or ‘Provisional’, depending upon its status in a peer review process.Final surveys are peer reviewed with extensive quality control methods to confirm that field attributes reflect the most detailed and specific land-use classification available, following the standard DWR Land Use Legendspecific to the survey year. Data sets are considered ‘final’ following the reconciliation of peer review comments and confirmation by the originating Regional Office. During final review, individual polygons are evaluated using a combination of aerial photointerpretation, satellite image multi-spectral data and time series analysis, comparison with other sources of land use data, and general knowledge of land use patterns at the local level.Provisional datasets have been reviewed for conformance with DWR’s published data record format, and for general agreement with other sources of land use trends. Comments based on peer review findings may not be reconciled, and no significant edits or changes are made to the original survey data.The 2009 El Dorado County land use survey data was developed by the State of California, Department of Water Resources (DWR) through its Division of Integrated Regional Water Management (DIRWM) and Division of Statewide Integrated Water Management (DSIWM). Land use boundaries were digitized and land use data was gathered by staff of DWR’s North Central Region using extensive field visits and aerial photography. Land use polygons in agricultural areas were mapped in greater detail than areas of urban or native vegetation. Quality control procedures were performed jointly by staff at DWR’s DSIWM headquarters, under the leadership of Jean Woods, and North Central Region, under the supervision of: Kim Rosmaier. This data was developed to monitor land use for the primary purpose of quantifying water use within this study area and determining changes in water use associated with land use changes over time. The associated data are considered DWR enterprise GIS data, which meet all appropriate requirements of the DWR Spatial Data Standards, specifically the DWR Spatial Data Standards version 2.1, dated March 9, 2016. DWR makes no warranties or guarantees - either expressed or implied - as to the completeness, accuracy, or correctness of the data. DWR neither accepts nor assumes liability arising from or for any incorrect, incomplete, or misleading subject data. Comments, problems, improvements, updates, or suggestions should be forwarded to gis@water.ca.gov. This data represents a land use survey of El Dorado County conducted by the California Department of Water Resources, North Central Regional Office staff. For digitizing, the county was subdivided into three areas using the centerline of U.S. Route 50 and a north/south line for boundaries. Land use field boundaries were digitized with ArcGIS 9.3 using 2005 U.S.D.A National Agriculture Imagery Program (NAIP) one-meter imagery as the base. Agricultural fields were delineated by following actual field boundaries instead of using the centerlines of roads to represent the field borders. The three digitized shapefiles were merged into a single file and the shared boundaries were removed. Field boundaries were reviewed and updated using 2009 NAIP imagery when it became available. Field boundaries were not drawn to represent legal parcel (ownership) boundaries, or meant to be used as parcel boundaries. The field work for this survey was conducted between the end of July and the first week of November 2009. Images, land use boundaries and ESRI ArcMap software, version 9.3 were loaded onto laptop computers that were used as the field data collection tools. Staff took these laptops into the field and virtually all agricultural fields were visited to positively identify the land use. Global positioning System (GPS) units connected to the laptops were used to confirm the surveyor's location with respect to the fields. Land use codes were digitized in the field using customized menus to enter land use attributes. The primary focus of this land use survey is mapping agricultural fields. Urban residences and other urban areas were delineated using aerial photo interpretation, so some urban areas may have been missed. Especially in rural residential areas, urban land use was delineated by drawing polygons to surround houses or other buildings along with a minimal area of land surrounding these structures. These footprint areas represent the locations of structures but do not represent the entire footprint of urban land. Information on sources of irrigation water was identified for general areas and occasionally supplemented by information obtained from landowners or by the observation of wells. Water source information was not collected for each field in the survey, so the water source listed for a specific agricultural field may not be accurate. Before final processing, standard quality control procedures were performed jointly by staff at DWR’s North Central Region, and at DSIWM headquarters under the leadership of Jean Woods. Senior Land and Water Use Supervisor. After quality control procedures were completed, the data was finalized. The positional accuracy of the digital line work, which is based upon the orthorectified NAIP imagery, is approximately 6 meters. The land use attribute accuracy for agricultural fields is high, because almost every delineated field was visited by a surveyor. The accuracy is 95 percent because some errors may have occurred. Possible sources of attribute errors are: a) Human error in the identification of crop types, b) Data entry errors.

This map is designated as Final.Land-Use Data Quality ControlEvery published digital survey is designated as either ‘Final’, or ‘Provisional’, depending upon its status in a peer review process. Final surveys are peer reviewed with extensive quality control methods to confirm that field attributes reflect the most detailed and specific land-use classification available, following the standard DWR Land Use Legendspecific to the survey year. Data sets are considered ‘final’ following the reconciliation of peer review comments and confirmation by the originating Regional Office. During final review, individual polygons are evaluated using a combination of aerial photointerpretation, satellite image multi-spectral data and time series analysis, comparison with other sources of land use data, and general knowledge of land use patterns at the local level.Provisional data sets have been reviewed for conformance with DWR’s published data record format, and for general agreement with other sources of land use trends. Comments based on peer review findings may not be reconciled, and no significant edits or changes are made to the original survey data.The 2013 Tuolumne County land use survey data was developed by the State of California, Department of Water Resources (DWR) through its Division of Integrated Regional Water Management (DIRWM) and Division of Statewide Integrated Water Management (DSIWM). Land use boundaries were digitized and land use data were gathered by staff of DWR’s North Central Region using extensive field visits and aerial photography. Land use polygons in agricultural areas were mapped in greater detail than areas of urban or native vegetation. Quality control procedures were performed jointly by staff at DWR’s DSIWM headquarters, under the leadership of Jean Woods, and North Central Region, under the supervision of Kim Rosmaier. This data was developed to aid DWR’s ongoing efforts to monitor land use for the main purpose of determining current and projected water uses. The associated data are considered DWR enterprise GIS data, which meet all appropriate requirements of the DWR Spatial Data Standards, specifically the DWR Spatial Data Standards version 2.1, dated March 9, 2016. DWR makes no warranties or guarantees - either expressed or implied - as to the completeness, accuracy, or correctness of the data. DWR neither accepts nor assumes liability arising from or for any incorrect, incomplete, or misleading subject data. Comments, problems, improvements, updates, or suggestions should be forwarded to gis@water.ca.gov. This data represents a land use survey of Tuolumne County conducted by the California Department of Water Resources, North Central Regional Office staff. Land use field boundaries were digitized with ArcGIS 10.0 and 10.2 using 2012 U.S.D.A National Agriculture Imagery Program (NAIP) one-meter imagery as the base. Agricultural fields were delineated by following actual field boundaries instead of using the centerlines of roads to represent the field borders. Field boundaries were reviewed and updated using 2013 Landsat 8 imagery. Field boundaries were not drawn to represent legal parcel (ownership) boundaries, and are not meant to be used as parcel boundaries. The field work for this survey was conducted during June 2013. Images, land use boundaries and ESRI ArcMap software were loaded onto laptop computers that were used as the field data collection tools. Staff took these laptops into the field and virtually all agricultural fields were visited to identify the land use. Global positioning System (GPS) units connected to the laptops were used to confirm the surveyor's location with respect to the fields. Land use codes were digitized in the field using dropdown selections from defined domains. Upon completion of the survey, a Python script was used to convert the data table into the standard land use format. ArcGIS geoprocessing tools and topology rules were used to locate errors for quality control. The primary focus of this land use survey is mapping agricultural fields. Urban residences and other urban areas were delineated using aerial photo interpretation. Some urban areas may have been missed, especially in forested areas. Rural residential land use was delineated by drawing polygons to surround houses and other buildings along with some of the surrounding land. These footprint areas do not represent the entire footprint of urban land. Sources of irrigation water were identified for general areas and occasionally supplemented by information obtained from landowners. Water source information was not collected for each field in the survey, so the water source listed for a specific agricultural field may not be accurate. Before final processing, standard quality control procedures were performed jointly by staff at DWR’s North Central Region, and at DSIWM headquarters under the leadership of Jean Woods. Senior Land and Water Use Supervisor. After quality control procedures were completed, the data was finalized. The positional accuracy of the digital line work, which is based upon the orthorectified NAIP imagery, is approximately 6 meters. The land use attribute accuracy for agricultural fields is high, because almost every delineated field was visited by a surveyor. The accuracy is 95 percent because some errors may have occurred. Possible sources of attribute errors are: a) Human error in the identification of crop types, b) Data entry errors.

Every published digital survey is designated as either ‘Final’, or ‘Provisional’, depending upon its status in a peer review process.Final surveys are peer reviewed with extensive quality control methods to confirm that field attributes reflect the most detailed and specific land-use classification available, following the standard DWR Land Use Legendspecific to the survey year. Data sets are considered ‘final’ following the reconciliation of peer review comments and confirmation by the originating Regional Office. During final review, individual polygons are evaluated using a combination of aerial photointerpretation, satellite image multi-spectral data and time series analysis, comparison with other sources of land use data, and general knowledge of land use patterns at the local level.Provisional data sets have been reviewed for conformance with DWR’s published data record format, and for general agreement with other sources of land use trends. Comments based on peer review findings may not be reconciled, and no significant edits or changes are made to the original survey data.The 2011 Marin County land use survey data was developed by the State of California, Department of Water Resources (DWR) through its Division of Integrated Regional Water Management (DIRWM) and Division of Statewide Integrated Water Management (DSIWM). Land use boundaries were digitized and land use data was gathered by staff of DWR’s North Central Region using extensive field visits and aerial photography. Land use polygons in agricultural areas were mapped in greater detail than areas of urban or native vegetation. Quality control procedures were performed jointly by staff at DWR’s DSIWM headquarters, under the leadership of Jean Woods, and North Central Region, under the supervision of Kim Rosmaier. This data was developed to aid DWR’s ongoing efforts to monitor land use for the main purpose of determining current and projected water uses. The associated data are considered DWR enterprise GIS data, which meet all appropriate requirements of the DWR Spatial Data Standards, specifically the DWR Spatial Data Standards version 2.1, dated March 9, 2016. DWR makes no warranties or guarantees - either expressed or implied - as to the completeness, accuracy, or correctness of the data. DWR neither accepts nor assumes liability arising from or for any incorrect, incomplete, or misleading subject data. Comments, problems, improvements, updates, or suggestions should be forwarded to gis@water.ca.gov. This data represents a land use survey of Marin County conducted by the California Department of Water Resources, North Central Regional Office staff. The field work for this survey was conducted during June 2011 by staff visiting each field and noting what was grown. Land use field boundaries were digitized using ArcGIS 9.3 then ArcGIS 10.0 using 2010 National Agriculture Imagery Program (NAIP) one-meter imagery as the base. To facilitate digitizing, Marin was divided in 2 portions, the Point Reyes area and all other areas of Marin County. These two areas were recombined after each portion was finished. The outer boundary of this land use survey coincides with the county line revisions completed by the California Department of Forestry and Fire Protection in 2009. Field boundaries were not drawn to represent legal parcel (ownership) boundaries, or meant to be used as parcel boundaries. Images and land use boundaries were loaded onto laptop computers that were used as the field data collection tools. Staff took these laptops into the field and virtually all the areas were visited to positively identify the land uses. Land use codes were digitized in the field using ESRI ArcMAP software, version 10.0. Global positioning system (GPS) units connected to the laptops were used to confirm the field team's location with respect to the fields. Staff took these laptops into the field and virtually all the areas were visited to positively identify the land uses. Land use codes were digitized in the field on laptop computers using ESRI ArcMAP software, version 10.0. The field team used a customized menu program to facilitate the gathering of field data. Before final processing, standard quality control procedures were performed jointly by staff at DWR’s North Central Region, and at DSIWM headquarters under the leadership of Jean Woods. Senior Land and Water Use Supervisor. After quality control procedures were completed, the data was finalized. The positional accuracy of the digital line work, which is based upon the orthorectified NAIP imagery, is approximately 6 meters. The land use attribute accuracy for agricultural fields is high, because almost every delineated field was visited by a surveyor. The accuracy is 95 percent because some errors may have occurred. Possible sources of attribute errors are: a) Human error in the identification of crop types, b) Data entry errors.

THE GEOINQUIRIES™ COLLECTION FOR U.S. History

http://www.esri.com/geoinquiries

The GeoInquiry™ collection for World History contains 15 free, standards-based activities that correspond and extend spatial concepts found in course textbooks frequently used in introductory world history classes. The activities use a common inquiry-based instructional model, require only 15 minutes to deliver, and are device/laptop agnostic. Each activity includes an ArcGIS Online map but requires no login or installation. The activities harmonize with the C3 Framework for social studies curriculum standards.

All World History GeoInquiries™ can be found at: http://esriurl.com/worldHistoryGeoInquiries

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

Every published digital survey is designated as either ‘Final’, or ‘Provisional’, depending upon its status in a peer review process. Final surveys are peer reviewed with extensive quality control methods to confirm that field attributes reflect the most detailed and specific land-use classification available, following the standard DWR Land Use Legendspecific to the survey year. Data sets are considered ‘final’ following the reconciliation of peer review comments and confirmation by the originating Regional Office. During final review, individual polygons are evaluated using a combination of aerial photointerpretation, satellite image multi-spectral data and time series analysis, comparison with other sources of land use data, and general knowledge of land use patterns at the local level.Provisional data sets have been reviewed for conformance with DWR’s published data record format, and for general agreement with other sources of land use trends. Comments based on peer review findings may not be reconciled, and no significant edits or changes are made to the original survey data.The 2013 Alpine County land use survey data was developed by the State of California, Department of Water Resources (DWR) through its Division of Integrated Regional Water Management (DIRWM) and Division of Statewide Integrated Water Management (DSIWM). Land use boundaries were digitized and land use data were gathered by staff of DWR’s North Central Region using extensive field visits and aerial photography. The land uses that were mapped were detailed agricultural land uses, and lesser detailed urban and native vegetation land uses. The land use data went through standard quality control procedures before final processing. Quality control procedures were performed jointly by staff at DWR’s DSIWM headquarters, under the leadership of Jean Woods, and North Central Region, under the supervision of Kim Rosmaier. This data was developed to aid DWR’s ongoing efforts to monitor land use for the main purpose of determining current and projected water uses. The associated data are considered DWR enterprise GIS data, which meet all appropriate requirements of the DWR Spatial Data Standards, specifically the DWR Spatial Data Standards version 2.1, dated March 9, 2016. DWR makes no warranties or guarantees - either expressed or implied - as to the completeness, accuracy, or correctness of the data. DWR neither accepts nor assumes liability arising from or for any incorrect, incomplete, or misleading subject data. Comments, problems, improvements, updates, or suggestions should be forwarded to gis@water.ca.gov. This data represents a land use survey of Alpine County conducted by the California Department of Water Resources, North Central Regional Office staff. Land use field boundaries were digitized with ArcGIS 10.0 and 10.2 using 2012 U.S.D.A National Agriculture Imagery Program (NAIP) one-meter imagery as the base. Agricultural fields were delineated by following actual field boundaries instead of using the centerlines of roads to represent the field borders. Field boundaries were reviewed and updated using 2013 Landsat 8 imagery. Field boundaries were not drawn to represent legal parcel (ownership) boundaries, and are not meant to be used as parcel boundaries. The field work for this survey was conducted during September 2013. Images, land use boundaries and ESRI ArcMap software were loaded onto laptop computers that were used as the field data collection tools. Staff took these laptops into the field and virtually all agricultural fields were visited to identify the land use. Global positioning System (GPS) units connected to the laptops were used to confirm the surveyor's location with respect to the fields. Land use codes were digitized in the field using dropdown selections from defined domains. Upon completion of the survey, a Python script was used to convert the data table into the standard land use format. ArcGIS geoprocessing tools and topology rules were used to locate errors for quality control. The primary focus of this land use survey is mapping agricultural fields. Urban residences and other urban areas were delineated using aerial photo interpretation. Some urban areas may have been missed, especially in forested areas. Rural residential land use was delineated by drawing polygons to surround houses and other buildings along with some of the surrounding land. These footprint areas do not represent the entire footprint of urban land. Sources of irrigation water were identified for general areas and occasionally supplemented by information obtained from landowners. Water source information was not collected for each field in the survey, so the water source listed for a specific agricultural field may not be accurate. After quality control procedures were completed, the data was finalized. The positional accuracy of the digital line work, which is based upon the orthorectified NAIP imagery, is approximately 6 meters. The land use attribute accuracy for agricultural fields is high, because almost every delineated field was visited by a surveyor. The accuracy is 95 percent because some errors may have occurred. Possible sources of attribute errors are: a) Human error in the identification of crop types, b) Data entry errors.

Every published digital survey is designated as either ‘Final’, or ‘Provisional’, depending upon its status in a peer review process. Final surveys are peer reviewed with extensive quality control methods to confirm that field attributes reflect the most detailed and specific land-use classification available, following the standard DWR Land Use Legendspecific to the survey year. Data sets are considered ‘final’ following the reconciliation of peer review comments and confirmation by the originating Regional Office. During final review, individual polygons are evaluated using a combination of aerial photointerpretation, satellite image multi-spectral data and time series analysis, comparison with other sources of land use data, and general knowledge of land use patterns at the local level.Provisional data sets have been reviewed for conformance with DWR’s published data record format, and for general agreement with other sources of land use trends. Comments based on peer review findings may not be reconciled, and no significant edits or changes are made to the original survey data.The 2010 northern Mono County land use survey data was developed by the State of California, Department of Water Resources (DWR) through its Division of Integrated Regional Water Management (DIRWM) and Division of Statewide Integrated Water Management (DSIWM). Land use boundaries were digitized and land use data was gathered by staff of DWR’s North Central Region using extensive field visits and aerial photography. Land use polygons in agricultural areas were mapped in greater detail than areas of urban or native vegetation. Quality control procedures were performed jointly by staff at DWR’s DSIWM headquarters, under the leadership of Jean Woods, and North Central Region, under the supervision of Kim Rosmaier. This data was developed to aid DWR’s ongoing efforts to monitor land use for the main purpose of determining current and projected water uses. The associated data are considered DWR enterprise GIS data, which meet all appropriate requirements of the DWR Spatial Data Standards, specifically the DWR Spatial Data Standards version 2.1, dated March 9, 2016. DWR makes no warranties or guarantees - either expressed or implied - as to the completeness, accuracy, or correctness of the data. DWR neither accepts nor assumes liability arising from or for any incorrect, incomplete, or misleading subject data. Comments, problems, improvements, updates, or suggestions should be forwarded to gis@water.ca.gov. These data represent a land use survey of northern Mono County conducted by the California Department of Water Resources, North Central Regional Office staff. The field work for this survey was conducted between July 12, 2010 and July 15, 2010 by staff visiting each field and noting what was grown. The survey field results are a snapshot in time of the crops and conditions of the study area visited. The southern boundary of the northern Mono County survey is the boundary between the North and South Lahontan Hydrologic Regions and does not include the Mono Lake area. Land use field boundaries were digitized using ArcGIS 9.3 then ArcGIS 10.0 using 2009 National Agriculture Imagery Program (NAIP) one-meter imagery as the base. Field boundaries were not drawn to represent legal parcel (ownership) boundaries, nor are they meant to be used as parcel boundaries. Images and land use boundaries were loaded onto laptop computers that were used as the field data collection tools. Staff took these laptops into the field and essentially all the areas were visited to positively identify the land uses. Land use codes were digitized in the field using ESRI ArcMAP software, version 10.0. Global positioning system (GPS) units connected to the laptops were used to confirm the field team's location with respect to the fields. The field team used a customized menu program to facilitate the gathering of field data. Before final processing, standard quality control procedures were performed jointly by staff at DWR’s North Central Region, and at DSIWM headquarters under the leadership of Jean Woods, Senior Land and Water Use Supervisor. Attributes and field borders were visually reviewed using 2010 NAIP and Landsat 5 imagery for quality control. Water boundaries were not updated to match the 2010 NAIP imagery. Landsat 5 image dates spanned the period from June 20, 2010 to October 10, 2010. After quality control procedures were completed, the data was finalized. The positional accuracy of the digital line work, which is based upon the orthorectified NAIP imagery, is approximately 6 meters. The land use attribute accuracy for agricultural fields is high, because almost every delineated field was visited by a surveyor. The accuracy is 95 percent because some errors may have occurred. Possible sources of attribute errors are: a) Human error in the identification of crop types, b) Data entry errors.

Every published digital survey is designated as either ‘Final’, or ‘Provisional’, depending upon its status in a peer review process. Final surveys are peer reviewed with extensive quality control methods to confirm that field attributes reflect the most detailed and specific land-use classification available, following the standard DWR Land Use Legendspecific to the survey year. Data sets are considered ‘final’ following the reconciliation of peer review comments and confirmation by the originating Regional Office. During final review, individual polygons are evaluated using a combination of aerial photointerpretation, satellite image multi-spectral data and time series analysis, comparison with other sources of land use data, and general knowledge of land use patterns at the local level.Provisional data sets have been reviewed for conformance with DWR’s published data record format, and for general agreement with other sources of land use trends. Comments based on peer review findings may not be reconciled, and no significant edits or changes are made to the original survey data.The 2012 Sonoma County land use survey data was developed by the State of California, Department of Water Resources (DWR) through its Division of Integrated Regional Water Management (DIRWM) and Division of Statewide Integrated Water Management (DSIWM). Land use boundaries were digitized and land use data was gathered by staff of DWR’s North Central Region using extensive field visits and aerial photography. Land use polygons in agricultural areas were mapped in greater detail than areas of urban or native vegetation. Quality control procedures were performed jointly by staff at DWR’s DSIWM headquarters, under the leadership of Jean Woods, and North Central Region, under the supervision of Kim Rosmaier. This data was developed to aid DWR’s ongoing efforts to monitor land use for the main purpose of determining current and projected water uses. The associated data are considered DWR enterprise GIS data, which meet all appropriate requirements of the DWR Spatial Data Standards, specifically the DWR Spatial Data Standards version 2.1, dated March 9, 2016. DWR makes no warranties or guarantees - either expressed or implied - as to the completeness, accuracy, or correctness of the data. DWR neither accepts nor assumes liability arising from or for any incorrect, incomplete, or misleading subject data. Comments, problems, improvements, updates, or suggestions should be forwarded to gis@water.ca.gov. This data represents a land use survey of Sonoma County conducted by the California Department of Water Resources, North Central Regional Office staff. The field work for this survey was conducted during July - September 2012 by staff visiting each field and noting what was grown. The county was divided into five survey areas using major road as centerlines and other geographic features for boundaries. The county was surveyed with two teams. The linework was heads up digitized in ArcGIS 10.0 with 2010 National Agriculture Imagery Program (NAIP) one-meter imagery as the base. Field Boundaries were reviewed with ArcGIS 10.2 and NAIP 2012 imagery when it became available. The data was recombined after it was finished. The Virtual Basic Landuse Attributor was used for the survey and to start the post survey process; after converting to ArcGIS 10.2, the domain file geodatabase structure was used to attribute and help finish facilitating the post survey process. Tables were run through a Python script to put the data in the standard landuse format. ArcGIS geoprocessing tools and topology rules were used to locate errors and for quality control and assurance. Horse pastures were designated either S2 or S6. The special condition 'G' was used to denote vineyards that had sprinklers for frost protection rather than representing a cover crop as stated in the February 2009 Standard Land Use Legend used for this survey. Field Boundaries were not drawn to represent legal parcel (ownership) boundaries, or meant to be used as parcel boundaries. Images and land use boundaries were loaded onto laptop computers that were used as the field data collection tools. GPS units connected to the laptops were used to confirm surveyor's location with respect to the fields. Staff took these laptops into the field and virtually all the areas were visited to positively identify the land use. Land use codes were digitized in the field on laptop computers using ESRI ArcMAP software, version 10.0. Before final processing, standard quality control procedures were performed jointly by staff at DWR’s North Central Region, and at DSIWM headquarters under the leadership of Jean Woods. Senior Land and Water Use Supervisor. After quality control procedures were completed, the data was finalized. The positional accuracy of the digital line work, which is based upon the orthorectified NAIP imagery, is approximately 6 meters. The land use attribute accuracy for agricultural fields is high, because almost every delineated field was visited by a surveyor. The accuracy is 95 percent because some errors may have occurred. Possible sources of attribute errors are: a) Human error in the identification of crop types, b) Data entry errors.

Every published digital survey is designated as either ‘Final’, or ‘Provisional’, depending upon its status in a peer review process.Final surveys are peer reviewed with extensive quality control methods to confirm that field attributes reflect the most detailed and specific land-use classification available, following the standard DWR Land Use Legendspecific to the survey year. Data sets are considered ‘final’ following the reconciliation of peer review comments and confirmation by the originating Regional Office. During final review, individual polygons are evaluated using a combination of aerial photointerpretation, satellite image multi-spectral data and time series analysis, comparison with other sources of land use data, and general knowledge of land use patterns at the local level.Provisional datasets have been reviewed for conformance with DWR’s published data record format, and for general agreement with other sources of land use trends. Comments based on peer review findings may not be reconciled, and no significant edits or changes are made to the original survey data.The 2009 El Dorado County land use survey data was developed by the State of California, Department of Water Resources (DWR) through its Division of Integrated Regional Water Management (DIRWM) and Division of Statewide Integrated Water Management (DSIWM). Land use boundaries were digitized and land use data was gathered by staff of DWR’s North Central Region using extensive field visits and aerial photography. Land use polygons in agricultural areas were mapped in greater detail than areas of urban or native vegetation. Quality control procedures were performed jointly by staff at DWR’s DSIWM headquarters, under the leadership of Jean Woods, and North Central Region, under the supervision of: Kim Rosmaier. This data was developed to monitor land use for the primary purpose of quantifying water use within this study area and determining changes in water use associated with land use changes over time. The associated data are considered DWR enterprise GIS data, which meet all appropriate requirements of the DWR Spatial Data Standards, specifically the DWR Spatial Data Standards version 2.1, dated March 9, 2016. DWR makes no warranties or guarantees - either expressed or implied - as to the completeness, accuracy, or correctness of the data. DWR neither accepts nor assumes liability arising from or for any incorrect, incomplete, or misleading subject data. Comments, problems, improvements, updates, or suggestions should be forwarded to gis@water.ca.gov. This data represents a land use survey of El Dorado County conducted by the California Department of Water Resources, North Central Regional Office staff. For digitizing, the county was subdivided into three areas using the centerline of U.S. Route 50 and a north/south line for boundaries. Land use field boundaries were digitized with ArcGIS 9.3 using 2005 U.S.D.A National Agriculture Imagery Program (NAIP) one-meter imagery as the base. Agricultural fields were delineated by following actual field boundaries instead of using the centerlines of roads to represent the field borders. The three digitized shapefiles were merged into a single file and the shared boundaries were removed. Field boundaries were reviewed and updated using 2009 NAIP imagery when it became available. Field boundaries were not drawn to represent legal parcel (ownership) boundaries, or meant to be used as parcel boundaries. The field work for this survey was conducted between the end of July and the first week of November 2009. Images, land use boundaries and ESRI ArcMap software, version 9.3 were loaded onto laptop computers that were used as the field data collection tools. Staff took these laptops into the field and virtually all agricultural fields were visited to positively identify the land use. Global positioning System (GPS) units connected to the laptops were used to confirm the surveyor's location with respect to the fields. Land use codes were digitized in the field using customized menus to enter land use attributes. The primary focus of this land use survey is mapping agricultural fields. Urban residences and other urban areas were delineated using aerial photo interpretation, so some urban areas may have been missed. Especially in rural residential areas, urban land use was delineated by drawing polygons to surround houses or other buildings along with a minimal area of land surrounding these structures. These footprint areas represent the locations of structures but do not represent the entire footprint of urban land. Information on sources of irrigation water was identified for general areas and occasionally supplemented by information obtained from landowners or by the observation of wells. Water source information was not collected for each field in the survey, so the water source listed for a specific agricultural field may not be accurate. Before final processing, standard quality control procedures were performed jointly by staff at DWR’s North Central Region, and at DSIWM headquarters under the leadership of Jean Woods. Senior Land and Water Use Supervisor. After quality control procedures were completed, the data was finalized. The positional accuracy of the digital line work, which is based upon the orthorectified NAIP imagery, is approximately 6 meters. The land use attribute accuracy for agricultural fields is high, because almost every delineated field was visited by a surveyor. The accuracy is 95 percent because some errors may have occurred. Possible sources of attribute errors are: a) Human error in the identification of crop types, b) Data entry errors.

The Mile Marker Inventory contains mile marker location information along Michigan's highways. Descriptive information for the mile markers include: latitude and longitude, route name, region, TSC, county, control section number, physical reference (PR) number, PR mile point, and mile number. The information was collected in 2011 using Framework V11. The data has not been updated and more current data may be available. Please see the MDOT Metadata Form for additional information. Update Cycle: The Mile Marker inventory was initially collected and completed in 2011 as part of the Lane Mile Inventory (LMI). There is currently no update plan in place to re-collect or update this inventory.Data Quality: Data was collected to +/- 25ft accuracy using a laptop equipped with ArcGIS Desktop and a tethered USB puck GPS unit.Coverage: This inventory is complete in its coverage of all State of Michigan trunkline roadways as of 2011.Symbology: The symbology for the Mile Markers is a simple point.Contact: Alonso Uzcategui, uzcateguia@michigan.gov

Site a water tower shared by two towns at the midpoint and determine the costs involved using the Pythagorean theorem. THE GEOINQUIRIES™ COLLECTION FOR MATHEMATICShttp://www.esri.com/geoinquiriesThe GeoInquiry™ collection for Mathematics contains 15 free, standards-based activities that correspond and extend spatial concepts found in course textbooks frequently used in introductory algebra or geometry classes. The activities use a common inquiry-based instructional model, require only 15 minutes to deliver, and are device/laptop agnostic. Each activity includes an ArcGIS Online map but requires no login or installation. The activities harmonize with the Common Core math national curriculum standards. Activities include:· Rates & Proportions: A lost beach· D=R x T· Linear rate of change: Steady growth· How much rain? Linear equations· Rates of population change· Distance and midpoint· The coordinate plane· Euclidean vs Non-Euclidean· Area and perimeter at the mall· Measuring crop circles· Area of complex figures· Similar triangles· Perpendicular bisectors· Centers of triangles· Volume of pyramids

Teachers, GeoMentors, and school administrators can learn more at http://www.esri.com/geoinquiries.

The Digital Divide Index or DDI ranges in value from 0 to 100, where 100 indicates the highest digital divide. It is composed of two scores, also ranging from 0 to 100: the infrastructure/adoption (INFA) score and the socioeconomic (SE) score.The INFA score groups five variables related to broadband infrastructure and adoption: (1) percentage of total 2020 population without access to fixed broadband of at least 100 Mbps download and 20 Mbps upload as of 2020 based on Ookla Speedtest® open dataset; (2) percent of homes without a computing device (desktops, laptops, smartphones, tablets, etc.); (3) percent of homes with no internet access (have no internet subscription, including cellular data plans or dial-up); (4) median maximum advertised download speeds; and (5) median maximum advertised upload speeds.The SE score groups five variables known to impact technology adoption: (1) percent population ages 65 and over; (2) percent population 25 and over with less than high school; (3) individual poverty rate; (4) percent of noninstitutionalized civilian population with a disability: and (5) a brand new digital inequality or internet income ratio measure (IIR). In other words, these variables indirectly measure adoption since they are potential predictors of lagging technology adoption or reinforcing existing inequalities that also affect adoption.These two scores are combined to calculate the overall DDI score. If a particular county or census tract has a higher INFA score versus a SE score, efforts should be made to improve broadband infrastructure. If on the other hand, a particular geography has a higher SE score versus an INFA score, efforts should be made to increase digital literacy and exposure to the technology’s benefits.The DDI measures primarily physical access/adoption and socioeconomic characteristics that may limit motivation, skills, and usage. Due to data limitations it was designed as a descriptive and pragmatic tool and is not intended to be comprehensive. Rather it should help initiate important discussions among community leaders and residents.Data for the digital divide index (DDI) was compiled by Purdue Center for Regional Development and obtained from the 5-year American Community Survey (ACS) and Ookla Speedtest® open dataset.

This data represents a land use survey of Sonoma County conducted by the California Department of Water Resources, North Central Regional Office staff. The field work for this survey was conducted during July - September 2012 by staff visiting each field and noting what was grown. The county was divided into five survey areas using major road as centerlines and other geographic features for boundaries. The county was surveyed with two teams. The linework was heads up digitized in ArcGIS 10.0 with 2010 National Agriculture Imagery Program (NAIP) one-meter imagery as the base. Field Boundaries were reviewed with ArcGIS 10.2 and NAIP 2012 imagery when it became available. The data was recombined after it was finished. The Virtual Basic Landuse Attributor was used for the survey and to start the post survey process; after converting to ArcGIS 10.2, the domain file geodatabase structure was used to attribute and help finish facilitating the post survey process. Tables were run through a Python script to put the data in the standard landuse format. ArcGIS geoprocessing tools and topology rules were used to locate errors and for quality control and assurance. Horse pastures were designated either S2 or S6. The special condition 'G' was used to denote vineyards that had sprinklers for frost protection rather than representing a cover crop as stated in the February 2009 Standard Land Use Legend used for this survey. Field Boundaries were not drawn to represent legal parcel (ownership) boundaries, or meant to be used as parcel boundaries.Images and land use boundaries were loaded onto laptop computers that were used as the field data collection tools. GPS units connected to the laptops were used to confirm surveyor's location with respect to the fields. Staff took these laptops into the field and virtually all the areas were visited to positively identify the land use. Land use codes were digitized in the field on laptop computers using ESRI ArcMAP software, version 10.0.Before final processing, standard quality control procedures were performed jointly by staff at DWR’s North Central Region, and at DSIWM headquarters under the leadership of Jean Woods. Senior Land and Water Use Supervisor. After quality control procedures were completed, the data was finalized. The positional accuracy of the digital line work, which is based upon the orthorectified NAIP imagery, is approximately 6 meters. The land use attribute accuracy for agricultural fields is high, because almost every delineated field was visited by a surveyor. The accuracy is 95 percent because some errors may have occurred. Possible sources of attribute errors are: a) Human error in the identification of crop types, b) Data entry errors.

Starting with the location of all Life Flight hospitals in a state, use perpendicular bisectors to draw boundaries between Life Flight regions, which will inform first responders.

THE GEOINQUIRIES™ COLLECTION FOR MATHEMATICS

http://www.esri.com/geoinquiries

The GeoInquiry™ collection for Mathematics contains 15 free, standards-based activities that correspond and extend spatial concepts found in course textbooks frequently used in introductory algebra or geometry classes. The activities use a common inquiry-based instructional model, require only 15 minutes to deliver, and are device/laptop agnostic. Each activity includes an ArcGIS Online map but requires no login or installation. The activities harmonize with the Common Core math national curriculum standards. Activities include:

· Rates & Proportions: A lost beach

· D=R x T

· Linear rate of change: Steady growth

· How much rain? Linear equations

· Rates of population change

· Distance and midpoint

· The coordinate plane

· Euclidean vs Non-Euclidean

· Area and perimeter at the mall

· Measuring crop circles

· Area of complex figures

· Similar triangles

· Perpendicular bisectors

· Centers of triangles

· Volume of pyramids

Teachers, GeoMentors, and school administrators can learn more at http://www.esri.com/geoinquiries.

Explore Chris McCandless’ journey into the wilds of Alaska and the factors that led to his death. Book: Into the Wild by Jon Krakauer. THE GEOINQUIRIES™ COLLECTION FOR AMERICAN LITERATUREhttp://www.esri.com/geoinquiriesThe GeoInquiry™ collection for American Literature contains 15 free, standards-based activities that correspond and extend map-based concepts found in course texts frequently used in high school literature. The activities use a common inquiry-based instructional model, require only 15 minutes to deliver, and are device/laptop agnostic. Each activity includes an ArcGIS Online map but requires no login or installation. The activities harmonize with the Common Core ELA national curriculum standards. Activities include:· Beyond religion: Scarlet Letter · Virus of fear: Witchcraft in Salem· Poe and the Red Death· The Red Badge of Courage· Twain: Travel blogger· Hurricane warning· Gatsby: Then and now· Our town, your town· The mockingbird sings for freedom· Depression, dust and Steinbeck· Hiroshima· Dr. King's road to a Birmingham aail· Finding Mango Street· F451: Ban or burn the books· Surviving the wild

Teachers, GeoMentors, and school administrators can learn more at http://www.esri.com/geoinquiries.