To open the notebook online in ArcGIS Notebooks, please visit https://www.arcgis.com/home/notebook/notebook.html?id=949a4fb10cf7450fb14a5ebbca746185 and sign in with an ArcGIS Online organizational account. To download the notebook (e.g., to use it with a notebook environment on your own computer), please visit the item page at https://edu.maps.arcgis.com/home/item.html?id=949a4fb10cf7450fb14a5ebbca746185 and click the Download button on the right.

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.

GIS_data_and_jupyter_python_notebook.zip: Data for Modeling SDS via Random Forest Models. Contains a ArcGIS Pro project with example data collected at Marston Farm (Boone, IA) and cropped Planet scope 4-band imagery of the area for 2016, 2017 and 2018.Preview for jupyter notebook: preview of a jupyter (Python 3) notebook that demonstrates the use of Random forest classifier using the GIS data.

ArcGIS Pro, verileri analiz etmek, haritalar oluşturmak ve görselleştirmek, modellemek ve otomatikleştirmek için çeşitli araçlar sunar. ArcGIS Pro içerisinde Notebook uygulaması, bu araçlardan biridir.Notebook, Python programlama dilini kullanarak CBS iş akışlarını gerçekleştirmek için interaktif bir ortamdır. Notebook, kod hücreleri ve metin hücreleri olarak adlandırılan iki tür hücreden oluşur. Kod hücreleri, Python kodunu çalıştırmak ve sonuçları görüntülemek için kullanılır. Metin hücreleri ise, kodun açıklamasını, belgelerini veya yorumlarını yazmak için kullanılır.Notebook kullanımının avantajları şunlardır:

We implemented automated workflows using Jupyter notebooks for each state. The GIS processing, crucial for merging, extracting, and projecting GeoTIFF data, was performed using ArcPy—a Python package for geographic data analysis, conversion, and management within ArcGIS (Toms, 2015). After generating state-scale LES (large extent spatial) datasets in GeoTIFF format, we utilized the xarray and rioxarray Python packages to convert GeoTIFF to NetCDF. Xarray is a Python package to work with multi-dimensional arrays and rioxarray is rasterio xarray extension. Rasterio is a Python library to read and write GeoTIFF and other raster formats. Xarray facilitated data manipulation and metadata addition in the NetCDF file, while rioxarray was used to save GeoTIFF as NetCDF. These procedures resulted in the creation of three HydroShare resources (HS 3, HS 4 and HS 5) for sharing state-scale LES datasets. Notably, due to licensing constraints with ArcGIS Pro, a commercial GIS software, the Jupyter notebook development was undertaken on a Windows OS.

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.

This Jupyter Notebook created by Laurence lin and Young-Don Choi to simulate the Paine Run subwatershed (12.7 km2) of Shenandoah National Park. This notebook shows how to create RHESssys input using grass GIS from GIS data, simulate RHESsys Model and visualize the output of RHESsys model.

An ArcGIS Notebook used by GIS professionals to archive joint use to a to another feature layer.

Author: Titus, Maxwell (mtitus@esri.com)Last Updated: 3/4/2025 Intended Environment: ArcGIS Notebooks on ArcGIS Online, ArcGIS Portal, or ArcGIS Pro. Purpose: This Notebook can batch share content from one group to another within ArcGIS Online Organization or an ArcGIS Portal. This does not require admin privileges to do this script and does not impact the original group having content moved. Requirements: Whoever runs the Notebook must have:They have access to both groups; where they share content from (i.e., the origin group) and (i.e., the target group).Can share content with the targeted group.

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 HydroShare resource contains the required GIS variables for building and running RHESSys models for any watershed with a valid gage at the Coweeta Hydrologic Laboratory. Contained in the .zip file below are custom datasets that include the gage shape file, 10m DEM, isohyet map, custom LAI map, and roads. Running RHESSys requires climate data which is also provided for the base climate station. For the purpose of demonstrating the accompanying Jupyter NoteBook, observed discharge data is included for WS18.

The associated Jupyter NoteBook resource can be dowloaded here: https://www.hydroshare.org/resource/081cbdb68415450b8ac99a5fe3092b5c/

Description: The user will provide the layer the wish to replace, and the layer they wish to replace it with. Then the notebook will loop through all the maps searching for the provided layer, and it will replace it with the other provided layer.Created on: 5/21/24Purpose: This is to streamline the ability to replace layers in webmaps in ArcGIS Online.Authored By: Joe GuziPrevious Production Date: 5/21/24Production Date: 6/25/24Note: You can use the ArcGIS Online Web Map Services Audit notebook, below, to get a preview of all of the maps that will be updated by this notebook. Simply run the notebook then filter on the layer you want to replace, and you will have an inventory of all of the maps that will be updated: https://www.arcgis.com/home/item.html?id=72ce7ff61fc5480d850ed68de29f1d9c

This repository contains the methods accompanying the paper 'A spatial model of cognitive distance in cities' (under review). The repository consists of three files.The main methods are found in the Python Jupyter notebook ('Cognitive Distance Anon'). This includes methods for estimating the effect of urban features (landmarks, land uses), intersections, turns, and network density on cognitive distance. The notebook clearly highlights the parameters used in defining the effect of each facet. The notebook also contains methods for calculating cognitive distance for a range of cities, drawing down GIS data for each city from OpenStreetMap using the OSMNx library. For each city, 500 random routes are calculated and distances extracted. The notebook contains additional methods relating to the generation of data visualisations used in the paper.The calculation of cognitive distance is supported by an additional functional package, landmark_functions.py. This code contains additional methods for landmark identification from OpenStreetMap data. This classification is based on the physical, pragmatic, and cultural components of each building within the dataset.The text file (test_cities.txt) contains the cities and coordinates used in estimation of cognitive distance, as documented in the paper.

This repository contains data and codes that support the findings of the study.- PPD-EPC open dataset with the enriched spatial analyses scores and UPRN.- Batch Geocoding Notebook of PPD-EPC dataset with GeoPy - Here API- PyQGIS codes for proximity, terrain, and visibility spatial analyses.- Jupyter Notebook of Machine Learning algorithms for mass property valuation.

Efficient Regionalization for Spatially Explicit Neighborhood Delineation## AbstractNeighborhood delineation is increasingly relied upon in urban social science research to identify the most appropriate spatial unit. In problems of this type, the true number of neighborhoods (typically called the k parameter) is unknown and analysts often require algorithmic approaches determine kendogenously. Existing approaches for neighborhood delineation that do not require pre-specification ofa k-parameter, however, are either nonspatial or lead to noncontiguous or overlapping regions. In this paper, we propose the use of max-p-regions for neighborhood delineation so that the geographic space can be partitioned into a set of homogeneous and geographically contiguous neighborhoods. In addition, we developed a new efficient algorithm to address the computational challenges associated with solving the max-p-regions so that it can be applied for large-scale neighborhood delineation. This new algorithm is implemented in the open-source Python Spatial Analysis Library (PySAL). Computational experiments based on both simulated and realistic data sets are performed and the results demonstrate its effectiveness and efficiency.## InstructionsThe files in this archive demonstrate the code for the new max-p algorithm.To run the demonstration, please do the following:1. extract the archive unzip maxp.zip2. Install Anaconda python distribution3. conda env create -f environment.yml4. conda activate maxp5. jupyter notebook6. Select the notebook demo.ipynb

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.

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 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.

This Hydroshare Resource contains a Jupyter notebook for building a RHESSys model using (1) a known USGS gage with (2) custom GIS data. It implements the interface of Jupyter notebooks and the functionality of RHESSys workflows to streamline model generation. There are two files as part of this resource that can be downloaded below under the Contents sections. The first is a .ipynb file that has complete instructions for uploading custom data, building RHESSys models in the cloud, and downloading models to your local machine. The second file is a .zip file of example data that can be used to step through the notebook. For more information on how to get started, open the jupyter notebook using the instructions below.

To open the jupyter notebook file file: (1) Download the .ipynb file below (2) From the Hydroshare homepage click APPS>Jupyter Python Notebook at NCSA (3) In the new window, click the Jupyter logo in the upper left (4) Open the "notebooks" folder and click "upload" in the upper right (5) Select the downloaded file from Step 1 and click the blue "Upload" button. (6) Select the newly uploaded RHESSysWorkflows.ipynb file to open the notebook.

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