ArcGIS Pro is Esri's main desktop GIS software and it is easy to enable student to install and use it on their personal laptops. All you have to do is:set students up with an Esri Identity in ArcGIS Onlinepoint student at the video explaining how to download ArcGIS ProStudent logs into ArcGIS Pro using their identityLets go through those steps in a bit more detail.

Maps have always been a powerful tool for visualizing data. Participants will learn how to link the static data of census tables to census geographies by using open-source GIS software. Participants will learn how to join data, calculate new attributes, symbolize geography and create maps. No prior GIS experience is necessary. QGIS will be required to be downloaded prior to the workshop, and laptops will be required. Download instructions https://qgis.org/en/site/forusers/download.html. Download data files https://drive.google.com/drive/folders/1xrAj_BrPtMDBgdi9MXWGcrcuVGfTsGgi?usp=sharing

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

The laptop touchscreen market is experiencing robust growth, driven by increasing demand for enhanced user experience and the integration of touch functionalities into various laptop models. The market, estimated at $15 billion in 2025, is projected to register a Compound Annual Growth Rate (CAGR) of 12% from 2025 to 2033. This growth is fueled by several key factors. The rising popularity of 2-in-1 laptops, which seamlessly blend laptop and tablet functionalities, is a major catalyst. Furthermore, advancements in touchscreen technology, including improved sensitivity, durability, and power efficiency, are making touchscreens more appealing to consumers and manufacturers alike. The integration of touchscreens with stylus support further expands the appeal, catering to creative professionals and students. Leading companies like Laibao Hi-Technology, TPK, ILJIN Display, GIS, Truly, Chung Hua EELY, DPT-Touch, MELFAS, and Henghao are actively shaping the market landscape through technological innovation and strategic partnerships. However, certain restraints are present. The relatively higher cost of manufacturing touchscreens compared to traditional input methods might limit market penetration, particularly in price-sensitive segments. Furthermore, concerns regarding screen durability and potential damage from scratches or impacts could pose challenges. Despite these challenges, the overall market outlook remains positive, with sustained growth predicted across various regions, especially in developing economies where increasing disposable incomes and rising digital adoption are driving demand for advanced computing devices. The market segmentation is likely to evolve further, with increasing specialization in different touchscreen technologies, form factors, and price points. This segmentation will create niche opportunities for manufacturers to target specific customer segments with specialized products.

In this asynchronous session, you will use some of the free GIS tools from the Teach With GIS website, created and maintained by the Esri UK education team. All of these tools are free to use and accessible as websites from laptops, tablets and mobile devices. We recommend that you view them on a laptop or tablet if possible, to give you plenty of screen space to see every detail. They do not require any logins or subscriptions. We want you to experience using modern, online GIS tools from the perspective of a student before you begin to create your own tools, maps, and lessons. We have chosen a range of tools that let you experience GIS as a tool to examine physical and human geography, and to compare and contrast over space and time.

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.

Responder Vehicle Rugged Computing Market Outlook

According to our latest research, the global Responder Vehicle Rugged Computing market size reached USD 4.28 billion in 2024. The market is expected to grow at a robust CAGR of 8.7% during the forecast period, reaching a projected value of USD 8.68 billion by 2033. The primary growth factors driving the market include the increasing need for reliable, durable computing solutions in harsh and unpredictable first responder environments, rising investments in public safety infrastructure, and the ongoing digital transformation of emergency response operations. As per the latest research, the market is witnessing a significant surge in demand due to advancements in rugged technology and the integration of IoT and AI in responder vehicles.

The growth of the Responder Vehicle Rugged Computing market is strongly influenced by the escalating complexities and challenges faced by modern emergency response teams. First responders such as police, fire, and medical personnel operate in environments where standard consumer-grade devices often fail due to exposure to water, dust, vibration, and extreme temperatures. The necessity for uninterrupted connectivity, real-time data access, and reliable communication tools has made rugged computing devices indispensable. Additionally, the increasing frequency of natural disasters, urbanization, and the evolving threat landscape have led governments and agencies to prioritize investments in ruggedized solutions to ensure operational continuity and safety.

Another significant growth factor is the rapid digitalization of public safety and emergency response workflows. The adoption of advanced technologies such as real-time video streaming, GIS mapping, telemedicine, and mobile command centers requires robust computing platforms capable of withstanding field conditions. Rugged laptops, tablets, and mobile computers are being integrated with sophisticated software to facilitate seamless information exchange and decision-making. The integration of 5G connectivity, AI-powered analytics, and cloud-based applications is further enhancing the efficiency and effectiveness of responder vehicle operations, fueling the demand for next-generation rugged computing devices.

Moreover, the market is benefiting from increased government funding and policy initiatives aimed at modernizing emergency response infrastructure. Many countries are implementing strategic programs to upgrade their public safety fleets with connected, data-driven technologies. This includes not only hardware procurement but also investments in training, cybersecurity, and lifecycle management. The trend toward interoperability and cross-agency collaboration is also driving the need for standardized, rugged computing platforms that can support multiple applications and communication protocols. As agencies continue to seek solutions that offer both durability and advanced functionality, the market for responder vehicle rugged computing is poised for sustained expansion.

Regionally, North America dominates the Responder Vehicle Rugged Computing market due to its advanced public safety infrastructure, high technology adoption rates, and significant budget allocations for emergency services. Europe follows closely, driven by stringent safety regulations and ongoing investments in smart city initiatives. The Asia Pacific region is emerging as a high-growth market, propelled by rapid urbanization, increasing focus on disaster management, and growing government initiatives to enhance emergency response capabilities. Latin America and the Middle East & Africa, while comparatively smaller, are witnessing rising adoption as regional governments prioritize resilience and modernization in their emergency response frameworks.

Product Type Analysis

The Product Type segment is a cornerstone of the Responder Vehicle Rugged Computing market, encompassing rugged laptops, rugged tablets, rugged mobile computers, rugged displays, and other specialized devices. Rugged laptops remain a preferred choice for many emergency responders due to their high processing power, full-sized keyboards, and compatibility with legacy software. These devices are engineered to withstand drops, spills, extreme temperatures, and continuous vibration, making them ideal for police cruisers, ambulances, and fire trucks. Manufacturers are continually innovating by introducing lighter, thinner models without compromis

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

Discover the first Industrial Revolution - birth and diffusion from 1750 to 1881. THE GEOINQUIRIES™ COLLECTION FOR WORLD HISTORYhttp://www.esri.com/geoinquiriesThe 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. Activities include:· Cradles of Civilization· Silk Roads: Then and now· Medieval Europe: Invasions· The Crusades· Trade and the Black Death· Russian expansion to the sea· Early European exploration· The Reformation· The first European Industrial Revolution· Latin American independence· Age of Napoleon· Africa's bounty and borders· Post-WWI and The League of Nations· African independence

Cooperation since 1945Teachers, GeoMentors, and school administrators can learn more at http://www.esri.com/geoinquiries.

This data represents a land use survey of San Mateo County conducted by the California Department of Water Resources, North Central Regional Office staff. The field work for this survey was conducted during June 2012 by staff visiting each field and noting what was grown. Land use field boundaries were digitized using ArcGIS 10.0 with 2010 National Agriculture Imagery Program (NAIP) one-meter imagery as the base. Field boundaries were reviewed and updated using 2012 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. 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.

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.

Examine the relationship between methods used to gain independence within Africa and political stability. THE GEOINQUIRIES™ COLLECTION FOR WORLD HISTORYhttp://www.esri.com/geoinquiriesThe 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. Activities include:· Cradles of Civilization· Silk Roads: Then and now· Medieval Europe: Invasions· The Crusades· Trade and the Black Death· Russian expansion to the sea· Early European exploration· The Reformation· The first European Industrial Revolution· Latin American independence· Age of Napoleon· Africa's bounty and borders· Post-WWI and The League of Nations· African independence

Cooperation since 1945Teachers, 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.

Learn about the Battle of Chancellorsville and how it is connected to Stephen Crane’s novel. 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.

Explore the effects of the Reformation and Counter-Reformation. THE GEOINQUIRIES™ COLLECTION FOR WORLD HISTORYhttp://www.esri.com/geoinquiriesThe 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. Activities include:· Cradles of Civilization· Silk Roads: Then and now· Medieval Europe: Invasions· The Crusades· Trade and the Black Death· Russian expansion to the sea· Early European exploration· The Reformation· The first European Industrial Revolution· Latin American independence· Age of Napoleon· Africa's bounty and borders· Post-WWI and The League of Nations· African independence

Cooperation since 1945Teachers, GeoMentors, and school administrators can learn more at http://www.esri.com/geoinquiries.

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.

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.

Explore the economic, environmental, and cultural influences in Steinbeck’s work. Particular attention paid to The Grapes of Wrath and Of Mice and Men.THE GEOINQUIRIES™ COLLECTION FOR AMERICAN LITERATUREhttps://esriurl.com/geoinquiries The 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:

This data represents a land use survey of Alameda County conducted by the California Department of Water Resources, Central District Office staff.Field survey boundaries data was developed using:1. The county was surveyed with a combination of 2005 one meter and 2006 two meter NAIP imagery. 2. The 2005 images were used in the spring of 2006 to develop the land use field boundary lines that would be used for the summer survey. The 2006 imagery was used for identification in the field and to edit any boundary line changes from the 2005 imagery. 3. These images and land use boundaries were copied onto laptop computers that were used as the field collection tools. The staff took these laptops in the field and virtually all areas were visited to positively identify the land use. The site visits occurred from June through September 2006. Land use codes were digitized directly into the laptop computers using AUTOCAD (and a standardized digitizing process) any land use boundaries changes were noted and corrected back in the office. 4. After quality control/assurance procedures were completed on each file (DWG), the data was finalized for the summer survey. Important points about using this data set:1. The land use boundaries were drawn on-screen using orthorectified imagery. They were drawn to depict observable areas of the same land use. They were not drawn to represent legal parcel (ownership) boundaries, or meant to be used as parcel boundaries. 2. This survey was created as a "snapshot" in the summer, and further improved by the addition of spring crops found through the use of satellite imagery. There still could be fields where there were crops grown before or after the field survey. The surveyor may not have been able to detect them from the field or the photographs, and the satellite imagery processing may not have identified the spring crop. Thus, although the data is very accurate for the summer, and probably the spring, it may not be an accurate determination of what was grown in the fields for the whole year. 3. If the data is to be brought into a GIS for analysis of copped (or planted) acreage, two things must be understood: a. The acreage of each field delineated is the gross area of the field. The amount of actual planted and irrigated acreage will always be less than the gross acreage, because of ditches, farm roads, other roads, farmsteads, etc. Thus, a delineated corn field may have a GIS calculated acreage of 40 acres but will have a smaller cropped (or net) acreage, maybe 38 acres. b. Double and multicropping must be taken into account. A delineated field of 40 acres might have been cropped first with grain, then with corn, and coded as such. To estimate actual cropped acres, the two crops are added together (38 acres of grain and 38 acres of corn) which results in a total of 76 acres of net crop (or planted) acres. 4. Not all land use codes will be represented in the survey. Sources of irrigation water were not identified. 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 12.4 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.