The global field data collection software market is experiencing robust growth, driven by the increasing need for efficient data management across diverse sectors. The market's expansion is fueled by several key factors: the rising adoption of mobile technologies and cloud-based solutions for improved data accessibility and real-time analysis; the increasing demand for automation in data collection processes to reduce manual errors and improve productivity; and the growing emphasis on data-driven decision-making across industries such as construction, environmental monitoring, and oil and gas. This shift towards digitalization is transforming traditional fieldwork practices, leading to enhanced accuracy, reduced operational costs, and improved overall efficiency. We estimate the market size in 2025 to be approximately $2.5 billion, with a Compound Annual Growth Rate (CAGR) of 15% projected through 2033. This growth is expected to be further fueled by advancements in AI and machine learning, which enhance data analysis capabilities and provide valuable insights from collected field data. While challenges remain, including concerns regarding data security and integration with existing systems, the overall market outlook remains positive, with significant opportunities for software vendors and service providers. The market segmentation reveals significant opportunities across various applications and deployment types. The cloud-based segment is experiencing the fastest growth, driven by its scalability, accessibility, and cost-effectiveness. The construction, environmental monitoring, and oil and gas sectors are major consumers of field data collection software, demonstrating a strong demand for solutions that streamline workflows, enhance safety protocols, and optimize resource allocation. Geographic analysis suggests North America and Europe are currently the largest markets, although the Asia-Pacific region is expected to witness substantial growth in the coming years due to increasing infrastructure development and industrialization. The competitive landscape is dynamic, with both established players and emerging startups offering specialized solutions. The success of these companies hinges on their ability to provide robust, user-friendly software with strong integration capabilities and advanced analytical features.

The CDPHE Respiratory Virus Immunization Data (Tableau Dashboard) dataset contains 2024-2025 COVID-19, Influenza, and RSV immunization data that have been administered to Colorado residents and reported to the Colorado Immunization Information System. The data in this file updates each Wednesday and includes the following fields:category: County, Demographics, Statewidesub_category: Age Group, Race/Ethnicity, Sex level: Current or Time Trendage_breakout: 0-7mo, 8-19mo, 6mo-17yr, 18-64, 60-74, 65+, 75+, Alldate: date for published data value (rate)vaccine: COVID-19, Flu, Nirsevimab, RSVmetric: context for published data value (rate)ratepublish_date: Data that this dataset was published to the CDPHE Open Data PortalFor more information, data definitions, and context, please visit the CDPHE Respiratory Virus Immunization Data website (https://cdphe.colorado.gov/respiratory-virus-immunization-data).

The population of Metro Vancouver (20110729Regional Growth Strategy Projections Population, Housing and Employment 2006 – 2041 File) will have increased greatly by 2040, and finding a new source of reservoirs for drinking water (2015_ Water Consumption_ Statistics File) will be essential. This issue of drinking water needs to be optimized and estimated (Data Mining file) with the aim of developing the region. Three current sources of water reservoirs for Metro Vancouver are Capilano, Seymour, and Coquitlam, in which the treated water is being supplied to the customer. The linear optimization (LP) model (Optimization, Sensitivity Report File) illustrates the amount of drinking water for each reservoir and region. In fact, the B.C. government has a specific strategy for the growing population till 2040, which leads them toward their goal. In addition, another factor is the new water source for drinking water that needs to be estimated and monitored to anticipate the feasible water source (wells) until 2040. As such, the government will have to make a decision on how much groundwater is used. The goal of the project is two steps: (1) an optimization model for three water reservoirs, and (2) estimating the new source of water to 2040.

The process of data analysis for the project includes: the data is analyzed with six software—Trifacta Wrangler, AMPL, Excel Solver, Arc GIS, and SQL—and is visualized in Tableau. 1. Trifacta Wrangler Software clean data (Data Mining file). 2. AMPL and Solver Excel Software optimize drinking water consumption for Metro Vancouver (data in the Optimization and Sensitivity Report file). 3. ArcMap collaborates the raw data and result of the optimization water reservoir and estimating population till 2040 with the ArcGIS software (GIS Map for Tableau file). 4. Visualizing, estimating, and optimizing the source of drinking water for Metro Vancouver until 2040 with SQL software in Tableau (export tableau data file).

Per California Water Code Section 10609.80 (a), DWR has released an update to the indicators analyzed for the rural communities water shortage vulnerability analysis and a new interactive tool to explore the data. This page remains to archive the original dataset, but for more current information, please see the following pages: - https://water.ca.gov/Programs/Water-Use-And-Efficiency/SB-552/SB-552-Tool - https://data.cnra.ca.gov/dataset/water-shortage-vulnerability-technical-methods - https://data.cnra.ca.gov/dataset/i07-water-shortage-vulnerability-sections - https://data.cnra.ca.gov/dataset/i07-water-shortage-social-vulnerability-blockgroup This dataset is made publicly available pursuant to California Water Code Section 10609.42 which directs the California Department of Water Resources to identify small water suppliers and rural communities that may be at risk of drought and water shortage vulnerability and propose to the Governor and Legislature recommendations and information in support of improving the drought preparedness of small water suppliers and rural communities. As of March 2021, two datasets are offered here for download. The background information, results synthesis, methods and all reports submitted to the legislature are available here: https://water.ca.gov/Programs/Water-Use-And-Efficiency/2018-Water-Conservation-Legislation/County-Drought-Planning Two online interactive dashboards are available here to explore the datasets and findings. https://dwr.maps.arcgis.com/apps/MapSeries/index.html?appid=3353b370f7844f468ca16b8316fa3c7b The following datasets are offered here for download and for those who want to explore the data in tabular format. (1) Small Water Suppliers: In total, 2,419 small water suppliers were examined for their relative risk of drought and water shortage. Of these, 2,244 are community water systems. The remaining 175 systems analyzed are small non-community non-transient water systems that serve schools for which there is available spatial information. This dataset contains the final risk score and individual risk factors for each supplier examined. Spatial boundaries of water suppliers' service areas were used to calculate the extent and severity of each suppliers' exposure to projected climate changes (temperature, wildfire, and sea level rise) and to current environmental conditions and events. The boundaries used to represent service areas are available for download from the California Drinking Water System Area Boundaries, located on the California State Geoportal, which is available online for download at https://gispublic.waterboards.ca.gov/portal/home/item.html?id=fbba842bf134497c9d611ad506ec48cc (2) Rural Communities: In total 4,987 communities, represented by US Census Block Groups, were analyzed for their relative risk of drought and water shortage. Communities with a record of one or more domestic well installed within the past 50 years are included in the analysis. Each community examined received a numeric risk score, which is derived from a set of indicators developed from a stakeholder process. Indicators used to estimate risk represented three key components: (1) the exposure of suppliers and communities to hazardous conditions and events, (2) the physical and social vulnerability of communities to the exposure, and (3) recent history of shortage and drought impacts. The unit of analysis for the rural communities, also referred to as "self-supplied communities" is U.S. Census Block Groups (ACS 2012-2016 Tiger Shapefile). The Census Block Groups do not necessarily represent socially-defined communities, but they do cover areas where population resides. Using this spatial unit for this analysis allows us to access demographic information that is otherwise not available in small geographic units.

The Location Analytics market is experiencing robust growth, projected to reach a significant size by 2033. A Compound Annual Growth Rate (CAGR) of 19.70% from 2019 to 2024 suggests a substantial increase in market value. This expansion is driven by several key factors. The increasing availability and affordability of location data from various sources, including GPS, mobile devices, and IoT sensors, fuels the market's growth. Businesses across diverse sectors are leveraging location analytics to gain actionable insights for improved operational efficiency, targeted marketing campaigns, and better customer experiences. Furthermore, advancements in data analytics technologies, such as AI and machine learning, are enabling more sophisticated location-based analysis, further driving market adoption. The emergence of cloud-based location analytics platforms enhances accessibility and scalability for businesses of all sizes. However, the market also faces certain challenges. Data privacy concerns and regulations, such as GDPR, pose significant restraints. The complexity of integrating various data sources and the need for skilled professionals to interpret and utilize the insights can also hinder wider market penetration. Despite these challenges, the long-term outlook for the location analytics market remains optimistic. The continued proliferation of connected devices and the rising demand for data-driven decision-making across industries will propel further growth. Market segmentation reveals strong performance across various sectors including retail, transportation and logistics, and government, with North America and Europe currently holding the largest market shares. The increasing adoption of location analytics in emerging economies like those within the Asia-Pacific region promises substantial future growth potential. The competitive landscape is characterized by a mix of established players, including Microsoft, Salesforce (through Tableau), and SAS, and emerging pure-play vendors like Google and TomTom, indicating a dynamic and innovative market. Recent developments include: November 2021 - Noogata announced the launch of its location analytics library. Building on the success of its existing e-commerce library, the location analytics library applies the power of Noogata's no code AI data analytics platform to physical locations for consumer packaged goods (CPG) brands., December 2020 - Microsoft unveiled a 'unified data governance service' as part of its Azure cloud platform, Azure Purview, that can discover and catalog all of an organization's data across all environments and locations.. Key drivers for this market are: Increasing Use of Spatial Data and Analytics in Various Industries, Growing Propensity of Consumers Toward Applications that Use Location Data. Potential restraints include: Increasing Use of Spatial Data and Analytics in Various Industries, Growing Propensity of Consumers Toward Applications that Use Location Data. Notable trends are: FMCG and E-Commerce Sector is Expected to Hold Significant Market Share.

Detroit Health Departments COVID-19 Dashboard that tracks cases and deaths over time, demographics, testing, hospital capacity, zip code level information, nursing home cases and deaths, and vaccination breakdowns.

This independent project examines the effects of the COVID-19 pandemic on the progress of Neglected Tropical Disease (NTD) elimination programs using raw data from the WHO database.Key Features and Tools:Data Analysis: Focused on treatment demand and fulfillment across multiple regions to assess the impact of disruptions caused by the pandemic.GIS Mapping: Incorporated geospatial visualizations to analyze regional disparities in treatment coverage and highlight vulnerable areas.Interactive Dashboards: Developed Tableau dashboards to compare pre- and post-pandemic treatment ratios, enabling users to explore trends and gaps in care provision.

The Location Intelligence Analytics market is experiencing robust growth, driven by the increasing need for businesses to leverage location data for strategic decision-making. The market's expansion is fueled by several key factors. Firstly, the proliferation of readily available location data from various sources, including GPS, mobile devices, and IoT sensors, provides rich insights for businesses across diverse sectors. Secondly, advancements in technologies like AI and machine learning are enhancing the analytical capabilities of location intelligence platforms, enabling more sophisticated predictions and optimized resource allocation. This is further amplified by the growing adoption of cloud-based solutions offering scalability and cost-effectiveness. Finally, the demand for real-time insights and personalized experiences is driving companies to incorporate location intelligence into their operations, ranging from supply chain optimization and targeted marketing to risk management and urban planning. We estimate the market size in 2025 to be approximately $15 billion, considering the rapid technological advancements and high adoption rates across various industries. A compound annual growth rate (CAGR) of 15% from 2025 to 2033 is projected, indicating significant market potential. However, despite the positive growth trajectory, the market faces certain challenges. Data privacy and security concerns are paramount, requiring robust compliance measures. The complexity of integrating disparate data sources and the need for skilled professionals to interpret the analytical outputs can hinder adoption for some businesses. Furthermore, the high initial investment costs associated with implementing location intelligence solutions may deter smaller organizations. Nevertheless, the strategic advantages of location intelligence are undeniable, and we expect the market to continue expanding significantly over the forecast period, with continued innovation in analytics technologies and expanding use cases driving its future growth. The competitive landscape is marked by a blend of established players like SAP, IBM, and Oracle, alongside emerging technology firms. This fosters innovation and provides a diverse range of solutions for businesses of all sizes.

This dashboard is hosted on Tableau Public, and was created from ONS data on annual internal migration flows between local authorities.This data was then processed to show net flows, and visualised with GIS data to create the dashboard.

Analysis of ‘COVID-19 Coronavirus Dataset’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/vignesh1694/covid19-coronavirus on 14 February 2022.

--- Dataset description provided by original source is as follows ---

Context

A SARS-like virus outbreak originating in Wuhan, China, is spreading into neighboring Asian countries, and as far afield as Australia, the US a and Europe.

On 31 December 2019, the Chinese authorities reported a case of pneumonia with an unknown cause in Wuhan, Hubei province, to the World Health Organisation (WHO)’s China Office. As more and more cases emerged, totaling 44 by 3 January, the country’s National Health Commission isolated the virus causing fever and flu-like symptoms and identified it as a novel coronavirus, now known to the WHO as 2019-nCoV.

The following dataset shows the numbers of spreading coronavirus across the globe.

Content

Sno - Serial number Date - Date of the observation Province / State - Province or state of the observation Country - Country of observation Last Update - Recent update (not accurate in terms of time) Confirmed - Number of confirmed cases Deaths - Number of death cases Recovered - Number of recovered cases

Acknowledgements

Thanks to John Hopkins CSSE for the live updates on Coronavirus and data streaming. Source: https://github.com/CSSEGISandData/COVID-19 Dashboard: https://public.tableau.com/profile/vignesh.coumarane#!/vizhome/DashboardToupload/Dashboard12

Inspiration

Inspired by the following work: https://gisanddata.maps.arcgis.com/apps/opsdashboard/index.html#/bda7594740fd40299423467b48e9ecf6

--- Original source retains full ownership of the source dataset ---

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Le tableau de permis d’appâts est un tableau connexe à celui de la zone de récolte d’appâts. Pour plus de détails et de métadonnées, consultez la zone de récolte d’appâts.Personne-resourceMae Rannells-Warren, Section des pêches, Direction des politiques relatives au poisson et à la faune, mae.rannells-warren@ontario.ca

Rocky Mountain Institute (RMI) Transportation Electrification Strategy (TES) Electric Vehicle Charging Station Layer displaying modeled need for EV stations by County within Washington State (2023-2035).The RMI TES Modeled Need layer is based on 2010 Counties from the U.S. Census Bureau.GridUp Tool: GridUp ToolTableau Data: https://public.tableau.com/app/profile/waevcouncil/viz/WashingtonTransportationElectrificationStrategy/Story_PublishedPlease direct questions about this item to partnerships@wsdot. If you are having trouble viewing this item, please email OnlineMapSupport@wsdot.wa.gov.

Tableau de bord de la situation COVID-19 de l'OMS

Le Land du Tyrol a publié son propre «tableau de bord des chiffres». Les chiffres et graphiques les plus importants sur la situation actuelle du coronavirus au Tyrol y sont présentés. Les chiffres sont mis à jour quatre fois par jour (08h30, 09h30, 13h30 et 18h30). Tous les chiffres présentés proviennent du système d'information du Land du Tyrol. Par conséquent, certaines différences par rapport aux chiffres publiés par le ministère de la Santé peuvent persister. Les résultats communaux des personnes actuellement atteintes du coronavirus sont présentés sur la base des chiffres qui peuvent être clairement attribués à une adresse et à un code postal dans le Land du Tyrol. Dans de rares cas, une affectation de district d'une personne testée positive est déjà possible à l'avance, mais une affectation à la commune à la date mise à jour n'est pas encore possible. Ainsi, il peut arriver que, dans l'intervalle, les résultats des communes et des districts ne soient pas totalement congruents au moment de la mise à jour respective. Des contrôles des données sont effectués en permanence par les services de santé et la Direction nationale de la santé publique.Ces flous à court terme sont donc toujours corrigés le plus rapidement possible.

Remarque: Les données statistiques ouvertes du Tyrol ne sont pas disponibles. Organisation de publication: Bureau du gouvernement du Land du Tyrol - Direction opérationnelle du Land L'application a été mise en œuvre avec le tableau de bord ArcGIS d'Esri.

Cet ensemble de données comprend la version Excel du Tableau 2.1 (Projections pour les précipitations extrêmes fondées sur de multiples méthodes) du rapport « Projections climatiques pour la région de la capitale nationale » (2020). Cette version du tableau comprend les trois horizons projectionnels (années 2030, 2050 et 2080).

Exactitude: Les mises en garde propres à l'indice sont précisées dans le rapport.

Fréquence des mises à jour: Téléversement ponctuel (2020)

Source d'information: Constatations recueillies pendant le projet

Courriel de l'auteur: Unité des changements climatiques et de la résilience

Aux termes de l'article 37 de la loi n° 2019-828 du 06 août 2019 de transformation de la fonction publique, les régions, les départements, les collectivités territoriales de plus de 80 000 habitants, les établissements publics de coopération intercommunale à fiscalité propre de plus de 80 000 habitants publient chaque année, sur leur site internet, la somme des dix rémunérations les plus élevées des agents relevant de leur périmètre, en précisant également le nombre de femmes et d'hommes figurant parmi ces dix rémunérations les plus élevées.

Classe d'entité localisant les parkings sur le territoire de la Métropole Toulon Provence Méditerranée (parking de surface, parking relais, parking en ouvrage / gratuit, payant, zone bleue, réglementé).

Visualiser une réutilisation des données par la Métropole TPM :

- Tableau de bord sur ...

Cette carte interactive, construite à partir de données INSEE, IGN et départementales, permet de visualiser différents périmètres administratifs et sociaux dans le Département de la Seine-Maritime en 2022.

il s'agit de quatre types de périmètres administratifs :

• les IRIS (Ilots Regroupés pour l'Information Statistique). Ce découpage, mis en place par l'INSEE, constitue la brique de base en matière de diffusion des données infra-communales. Un IRIS représente entre 1 800 et 5 000 habitants ou plus de 1 000 salariés ou une emprise spécifique peu peuplée (port, parc naturel, etc.),
• les communes,

• les intercommunalités (Établissements Publics de Coopération Intercommunales ou EPCI), qui sont des regroupements de communes autour de projets communs : Communautés de Communes, Communautés d'Agglomération, Métropoles, etc.,

• les cantons, qui sont les circonscriptions servant de cadre à l'élection des conseillers départementaux,

ainsi que trois sectorisations correspondants à des échelles de mise en œuvre des politiques sociales du Département :

• les secteurs des Centres Médico-Sociaux (CMS), structures départementales qui assurent le suivi médical des bébés et des jeunes enfants mais constitue également un point d'entrée local pour l'accès aux droits (insertion professionnelle, soutien aux personnes âgées, etc.)
• les groupements de CMS, qui constituent des périmètres à visée plus technique utilisés par le Département pour la mise en œuvre de ses politiques sociales,
• et les Unités territoriales d'Action sociale (UTAS), le Département est divisé en 5 UTAS, qui sont tant des lieux d'accueil, d'information, d'orientation et d'accompagnement des publics que des périmètre de réflexion dotés de spécificités sociologiques et territoriales.

L'utilisateur peut, en cliquant sur le nom d'une commune puis, en sélectionnant le ou les choix qui apparaissent automatiquement dans les menus déroulants correspondants, visualiser à quels EPCI, Canton, CMS, groupement de CMS et UTAS ce territoire appartient. La recherche est également possible à partir des autres échelons : EPCI, CMS, etc.Enfin, un clic droit sur la carte indiquera dans une fenêtre pop-up les noms des différents périmètres auxquels l’endroit cliqué appartient (appuyer sur les flèches en haut à droite de la fenêtre pour les faire défiler). Pour plus de lisibilité, vous pouvez aussi désactiver la sélection d’une ou de plusieurs catégories en cliquant sur le bouton « réinitialiseré (en bas de la catégorie) pour ne laisser visibles que celles qui vous intéressent.

Métadonnées

Lien vers les métadonnées

Ressources complémentaires

• Site de l'INSEE : https://www.insee.fr/fr/accueil

Le site de l'Institut National de la Statistique et des Études Économiques fournit des définitions détaillées des différents périmètres administratifs français et permet également de télécharger de nombreuses données à ces échelles.

• Site Géoservices : https://geoservices.ign.fr/

De nombreuses données de l'institut national de l'information géographique et forestière sont librement téléchargeables, notamment au format shape, sur ce site édité par l'IGN.

• Site du Département de la Seine-Maritime : https://www.seinemaritime.fr/mon-quotidien/sante/les-centres-medico-sociaux-.html et https://www.seinemaritime.fr/mon-departement/le-territoire/les-utas.html

Le Site du Département de la Seine-Maritime fournit plus d'informations sur le rôle des CMS et des UTAS et les prestations qu'ils sont susceptibles d'offrir.

Ce jeu de données a servi à la réalisation de ce tableau de bord interactif. Il recense les acteurs publics (collectivités, services déconcentrés de l'Etat, etc) et parapublics (association, entreprises assurant une mission de service public, etc) identifiés comme engagés dans l'open data. Est considéré comme " engagé " un acteur qui : * publie ses données sur un portail dédié ou /et, * publie ses données sur un portail mutualisé avec un ou plusieurs autres acteurs ou / et, * publie ses données sur un portail national tel que data.gouv.fr. La description des attributs est téléchargeable ici.