Location Intelligence and Location Analytics Market Outlook

The global market size for Location Intelligence (LI) and Location Analytics is projected to grow from $XX billion in 2023 to $XX billion by 2032, exhibiting a CAGR of XX%. This remarkable growth is driven by the increasing adoption of geospatial data in business operations and the rising demand for location-based services in various industries.

One of the primary growth factors for the Location Intelligence and Location Analytics market is the proliferation of Internet of Things (IoT) devices. These devices generate vast amounts of location-based data that can be analyzed to provide valuable insights. Companies are increasingly recognizing the importance of leveraging this data to enhance operational efficiency, improve customer experience, and drive strategic decision-making. The integration of artificial intelligence (AI) and machine learning (ML) with Location Analytics further enhances the ability to process and analyze large datasets, providing more accurate and actionable insights.

Another significant driver is the growing need for real-time location-based services. In sectors such as retail, transportation, and logistics, real-time location analytics enable businesses to track assets, monitor workforce movements, and manage facilities more effectively. This real-time data helps in optimizing routes, reducing fuel consumption, and improving overall productivity. Additionally, the COVID-19 pandemic has accelerated the adoption of location-based services for contact tracing, social distancing monitoring, and ensuring workplace safety, further propelling market growth.

Advancements in geographic information systems (GIS) and the increasing availability of high-resolution satellite imagery are also contributing to market expansion. Modern GIS platforms offer sophisticated tools for spatial analysis, mapping, and visualization, enabling organizations to derive meaningful insights from complex geospatial data. The integration of location analytics with business intelligence (BI) tools allows for comprehensive analysis and visualization of data, leading to better strategic planning and decision-making.

Regionally, North America is expected to hold the largest market share, driven by the presence of major technology companies and early adoption of advanced technologies. The Asia Pacific region is anticipated to witness the highest growth rate, fueled by rapid urbanization, increasing investments in smart city projects, and the expanding e-commerce sector. Europe, Latin America, and the Middle East & Africa are also expected to contribute significantly to the market growth, with various industries adopting location-based services to enhance operational efficiency and customer engagement.

Component Analysis

The Location Intelligence and Location Analytics market is segmented into two main components: Software and Services. The Software segment dominates the market, driven by the increasing demand for sophisticated analytics tools that can process and visualize geospatial data. Advanced software solutions offer capabilities such as spatial analysis, mapping, and real-time data processing, enabling businesses to gain deeper insights into their operations and customer behavior. The integration of AI and ML with location analytics software further enhances its analytical capabilities, making it a crucial component for businesses seeking to leverage geospatial data.

Within the Software segment, geographic information systems (GIS) and business intelligence (BI) tools play a pivotal role. GIS platforms provide extensive functionalities for spatial data analysis, mapping, and visualization, allowing organizations to derive actionable insights from complex datasets. The integration of BI tools with location analytics enables businesses to perform comprehensive analyses and generate interactive dashboards, facilitating informed decision-making. The increasing adoption of cloud-based software solutions is also driving market growth, offering scalability, flexibility, and cost-effectiveness to businesses of all sizes.

The Services segment encompasses various professional and managed services that support the deployment and utilization of location analytics solutions. Consulting services assist organizations in identifying their specific needs and developing customized solutions, while implementation services ensure seamless integration of location analytics tools with existing systems. Managed services provide ongoing support, maintenance, and optimization of location analy

COVID-19: Army Corps Uses Maps and Models to Create Surge Hospital CapacityAfter recognizing the possibility that the COVID-19 pandemic could cause hospital bed capacity to be exceeded, the US Army Corps of Engineers (USACE) was tasked with working with the states to build and inspect alternate care facilities.A team from USACE developed engineering plans for converting existing facilities with rooms (such as hotels or college dormitories) and those with large open areas (like field houses or convention centers). From there, the team developed standardized designs, then used mobile applications to quickly assess candidate sites and inspect the retrofitted facilities for readiness._Communities around the world are taking strides in mitigating the threat that COVID-19 (coronavirus) poses. Geography and location analysis have a crucial role in better understanding this evolving pandemic.When you need help quickly, Esri can provide data, software, configurable applications, and technical support for your emergency GIS operations. Use GIS to rapidly access and visualize mission-critical information. Get the information you need quickly, in a way that’s easy to understand, to make better decisions during a crisis.Esri’s Disaster Response Program (DRP) assists with disasters worldwide as part of our corporate citizenship. We support response and relief efforts with GIS technology and expertise.More information...

According to Cognitive Market Research, the global Geographic Information System market size will be USD 10215.6 million in 2024. It will expand at a compound annual growth rate (CAGR) of 9.20% from 2024 to 2031.

North America held the major market share for more than 40% of the global revenue with a market size of USD 4086.24 million in 2024 and will grow at a compound annual growth rate (CAGR) of 7.4% from 2024 to 2031.
Europe accounted for a market share of over 30% of the global revenue with a market size of USD 3064.68 million.
Asia Pacific held a market share of around 23% of the global revenue with a market size of USD 2349.59 million in 2024 and will grow at a compound annual growth rate (CAGR) of 11.2% from 2024 to 2031.
Latin America had a market share of more than 5% of the global revenue with a market size of USD 510.78 million in 2024 and will grow at a compound annual growth rate (CAGR) of 8.6% from 2024 to 2031.
Middle East and Africa had a market share of around 2% of the global revenue and was estimated at a market size of USD 204.31 million in 2024 and will grow at a compound annual growth rate (CAGR) of 8.9% from 2024 to 2031.
The government category is the fastest growing segment of the Geographic Information System industry

Market Dynamics of Geographic Information System Market

Key Drivers for Geographic Information System Market

Increased Demand for Location-Based Services to Boost Market Growth

The market for geographic information systems (GIS) is expanding due in large part to the growing demand for location-based services (LBS). Retail, transportation, and logistics are just a few of the businesses that are adopting LBS applications like navigation, geotagging, and real-time tracking. Businesses use GIS-enabled LBS to improve operational efficiency, optimize delivery routes, and monitor customer behavior. Furthermore, GIS-powered LBS is now more widely available because of developments in smartphone technology and the growth of IoT devices. As a result of urbanization and smart city projects, governments and organizations are using GIS to manage resources and build cities based on location. In the upcoming years, the GIS market is expected to develop dramatically due to this increased reliance on LBS.

Advancements in Geospatial Technology to Drive Market Growth

The Geographic Information System (GIS) industry is expanding significantly due to advancements in geospatial technologies. Technologies like LiDAR, remote sensing, and 3D mapping have completely changed how spatial data is collected, processed, and shown. More accurate and useful insights are made possible by improved real-time data processing and AI integration capabilities, which help sectors including disaster relief, agriculture, and urban planning. GIS applications are being further transformed by emerging technologies like virtual reality (VR) and augmented reality (AR), which enable immersive data visualization and better decision-making. These developments in technology, along with the falling prices of geospatial tools, are increasing the use of GIS in various industries and driving global market expansion.

Restraint Factor for the Geographic Information System Market

Data Privacy and Security Concerns Will Limit Market Growth

Data security and privacy issues are major barriers to the Geographic Information System (GIS) market's expansion. GIS applications frequently incorporate sensitive location-based data, including information on natural resources, infrastructure design, and human movements. Potential data breaches, illegal access, and abuse present serious privacy and cybersecurity issues. When strong data protection measures are not in place, governments and organizations are reluctant to employ GIS systems. Variable international data privacy laws, like the GDPR in Europe, also make the implementation of GIS systems more challenging. For these issues to be resolved and for GIS technologies to be widely adopted, it is imperative that geographical data be processed, stored, and shared securely.

Impact of Covid-19 on the Geographic Information System Market

The Geographic Information System (GIS) business was greatly impacted by the COVID-19 epidemic, which led to a rise in adoption across a number of industries. Governments and medical institutions use GIS to plan vaccination campaigns, allocate resources, and follow the spread of viruses in real-time. GIS...

On March 2, 2022 DC Health announced the District’s new COVID-19 Community Level key metrics and reporting. COVID-19 cases are now reported on a weekly basis. The data in this table includes overall COVID-19 statistics for the District of Columbia hospitals. The number of hospital beds and ventilators available. Due to rapidly changing nature of COVID-19, data for March 2020 is limited.General Guidelines for Interpreting Disease Surveillance Data during a disease outbreak, the health department will collect, process, and analyze large amounts of information to understand and respond to the health impacts of the disease and its transmission in the community. The sources of disease surveillance information include contact tracing, medical record review, and laboratory information, and are considered protected health information. When interpreting the results of these analyses, it is important to keep in mind that the disease surveillance system may not capture the full picture of the outbreak, and that previously reported data may change over time as it undergoes data quality review or as additional information is added. These analyses, especially within populations with small samples, may be subject to large amounts of variation from day to day. Despite these limitations, data from disease surveillance is a valuable source of information to understand how to stop the spread of COVID19.

On March 2, 2022 DC Health announced the District’s new COVID-19 Community Level key metrics and reporting. COVID-19 cases are now reported on a weekly basis. More information available at https://coronavirus.dc.gov. District of Columbia Child and Family Services Agency testing for the number of positive tests, quarantined, returned to work and lives lost. Due to rapidly changing nature of COVID-19, data for March 2020 is limited.General Guidelines for Interpreting Disease Surveillance DataDuring a disease outbreak, the health department will collect, process, and analyze large amounts of information to understand and respond to the health impacts of the disease and its transmission in the community. The sources of disease surveillance information include contact tracing, medical record review, and laboratory information, and are considered protected health information. When interpreting the results of these analyses, it is important to keep in mind that the disease surveillance system may not capture the full picture of the outbreak, and that previously reported data may change over time as it undergoes data quality review or as additional information is added. These analyses, especially within populations with small samples, may be subject to large amounts of variation from day to day. Despite these limitations, data from disease surveillance is a valuable source of information to understand how to stop the spread of COVID19.

MethodsWe relied on reports of confirmed case incidence and test positivity, along with data on the movements of devices with location-tracking software, to evaluate a novel scheme of three concentric regulatory zones introduced by then New York Governor Cuomo to address an outbreak of COVID-19 in South Brooklyn in the fall of 2020. The regulatory scheme imposed differential controls on access to eating places, schools, houses of worship, large gatherings and other businesses within the three zones, but without restrictions on mobility.ResultsWithin the central red zone, COVID-19 incidence temporarily declined from 131.2 per 100,000 population during the week ending October 3 to 62.5 per 100,000 by the week ending October 31, but then rebounded to 153.6 per 100,000 by the week ending November 28. Within the intermediate orange and peripheral yellow zones combined, incidence steadily rose from 28.8 per 100,000 during the week ending October 3 to 109.9 per 100,000 by the week ending November 28. Data on device visits to pairs of eating establishments straddling the red-orange boundary confirmed compliance with access controls. More general analysis of device movements showed stable patterns of movement between and beyond zones unaffected by the Governor's orders. A geospatial regression model of COVID-19 incidence in relation to device movements across zip code tabulation areas identified a cluster of five high-movement ZCTAs with estimated reproduction number 1.91 (95% confidence interval, 1.27–2.55).DiscussionIn the highly populous area of South Brooklyn, controls on access alone, without restrictions on movement, were inadequate to halt an advancing COVID-19 outbreak.

Cloud Based Mapping Service Market Outlook

The global cloud-based mapping service market size was valued at approximately USD 1.2 billion in 2023 and is projected to reach around USD 3.5 billion by 2032, growing at a compound annual growth rate (CAGR) of 12.5% during the forecast period. This growth is primarily driven by the increasing demand for real-time location data and asset tracking across various industries, such as transportation and logistics, retail, and healthcare.

One of the primary growth factors in the cloud-based mapping service market is the widespread adoption of Internet of Things (IoT) devices, which generate enormous amounts of location-based data. Enterprises are increasingly leveraging cloud-based mapping services to process and visualize this data for better decision-making and operational efficiency. Additionally, the proliferation of smartphones and mobile applications has fueled the demand for geospatial services, further propelling market growth. Moreover, the advancements in artificial intelligence (AI) and machine learning are enhancing the capabilities of mapping services, making them more sophisticated and reliable.

Another significant growth driver is the rising need for efficient fleet management solutions, particularly in the transportation and logistics sector. Companies are investing heavily in cloud-based mapping services to optimize routes, reduce fuel consumption, and improve delivery times. Furthermore, governments and defense organizations are increasingly adopting these services for surveillance, border security, and disaster management, further boosting the market. The integration of cloud-based mapping with Geographic Information Systems (GIS) is also providing valuable insights for urban planning and infrastructure development.

The healthcare sector is also emerging as a critical end-user of cloud-based mapping services. Hospitals and emergency services are utilizing these services for better patient tracking, resource allocation, and route optimization for ambulances. The COVID-19 pandemic has further accelerated the adoption of these technologies, as they play a crucial role in contact tracing and managing healthcare logistics. Retail businesses are also increasingly adopting cloud-based mapping services to enhance their supply chain management, optimize store layouts, and offer personalized customer experiences.

The advent of Mobile Mapping technology is revolutionizing the way geospatial data is collected and utilized. Mobile Mapping involves the use of vehicles equipped with various sensors, such as cameras and LiDAR, to capture detailed spatial data while in motion. This technology is particularly beneficial for creating accurate maps of urban environments, as it allows for the rapid collection of data over large areas. The integration of Mobile Mapping with cloud-based services enhances the ability to process and analyze this data in real-time, providing valuable insights for applications such as urban planning, infrastructure development, and environmental monitoring. As industries continue to seek efficient and cost-effective mapping solutions, the demand for Mobile Mapping is expected to grow, further driving the expansion of the cloud-based mapping service market.

Regionally, North America holds a significant share of the cloud-based mapping service market, primarily due to the early adoption of advanced technologies and the presence of major industry players. However, the Asia Pacific region is expected to witness the highest growth rate during the forecast period. This growth can be attributed to rapid digital transformation, increasing investments in smart city projects, and the rising adoption of IoT devices in countries like China and India. Europe is also expected to show steady growth, driven by advancements in automotive technologies and increasing government initiatives for digitalization.

Component Analysis

The cloud-based mapping service market can be segmented into software and services based on components. The software segment includes various mapping and geospatial analysis tools that allow users to gather, analyze, and visualize location-based data. This segment is experiencing significant growth due to the increasing demand for advanced mapping solutions that offer real-time data and enhanced analytics. The proliferation of mobile applications that require geospatial data is also driving the growth of the software segment. Furthermore, the integration of AI and ML algorithms into

NOTE THIS LAYER IS DEPRECATED (last updated 6/7/2022). This was formerly a weekly update. Summary The cumulative total of confirmed COVID-19 cases that have been entered into covidLINK and have been reached for contact tracing interviews as of the date of report. Description As of 12/29/21, contact tracing data, including case counts and percentages, reflect fluctuations in case reporting resulting from a network security incident. In addition, high risk location and large gathering questions were removed from the contact tracing interview questionnaire, as the questions no longer provided actionable information on potential exposure locations due to high levels of COVID-19 community transmission. As of 1/21/22, MDH implemented a new outbound email and text-based contact tracing web survey procedure, reflected in ""Outreach within 24 hours"" metrics." The MD COVID-19 - Contact Tracing Cases Reached and Interviewed data layer reflects the cumulative total of confirmed COVID-19 cases that have been entered into covidLINK and been reached for contact tracing interviews as of the date of report. Individuals that responded to outreach attempts and were verified as the intended call recipient are considered successfully reached. Cases are considered interviewed if they have completed the case investigation interview. Not responding to calls is the primary reason cases are not successfully reached. For cases reached, reasons for not completing an interview include scheduling conflict, hospitalization/incapacitation, and refusal to participate. Data are updated weekly on Wednesday during the 10 a.m. hour (data is reported through the previous Saturday). Terms of Use The Spatial Data, and the information therein, (collectively the "Data") is provided "as is" without warranty of any kind, either expressed, implied, or statutory. The user assumes the entire risk as to quality and performance of the Data. No guarantee of accuracy is granted, nor is any responsibility for reliance thereon assumed. In no event shall the State of Maryland be liable for direct, indirect, incidental, consequential or special damages of any kind. The State of Maryland does not accept liability for any damages or misrepresentation caused by inaccuracies in the Data or as a result to changes to the Data, nor is there responsibility assumed to maintain the Data in any manner or form. The Data can be freely distributed as long as the metadata entry is not modified or deleted. Any data derived from the Data must acknowledge the State of Maryland in the metadata.

According to Cognitive Market Research, the global GIS in Telecom Sector market size is USD XX million in 2024. It will expand at a compound annual growth rate (CAGR) of 15.00% from 2024 to 2031.

North America held the major market share for more than 40% of the global revenue with a market size of USD XX million in 2024 and will grow at a compound annual growth rate (CAGR) of 13.2% from 2024 to 2031.
Europe accounted for a market share of over 30% of the global revenue with a market size of USD XX million.
Asia Pacific held a market share of around 23% of the global revenue with a market size of USD XX million in 2024 and will grow at a compound annual growth rate (CAGR) of 17.0% from 2024 to 2031.
Latin America had a market share for more than 5% of the global revenue with a market size of USD XX million in 2024 and will grow at a compound annual growth rate (CAGR) of 14.4% from 2024 to 2031.
Middle East and Africa hada market share of around 2% of the global revenue and was estimated at a market size of USD XX million in 2024 and will grow at a compound annual growth rate (CAGR) of 14.7% from 2024 to 2031.
Large enterprises dominate the GIS in telecom sector market as primary end-users. These companies possess extensive telecom infrastructure and resources, making them ideal candidates for comprehensive GIS solutions.

Market Dynamics of GIS in Telecom Sector Market

Key Drivers for GIS in Telecom Sector Market

Increasing Demand for Efficient Network Planning and Optimization Solutions to Increase the Demand Globally

One of the primary drivers of the GIS in the telecom sector market is the escalating need for efficient network planning and optimization solutions. As telecom operators strive to enhance their network coverage and capacity, GIS technology plays a crucial role in providing detailed spatial analysis and visualization. This enables telecom companies to plan and optimize their networks more accurately, reducing operational costs and improving service quality. The capability of GIS to integrate various data layers, such as demographic, topographic, and network performance data, allows for precise decision-making and strategic deployment of resources, thereby driving its adoption in the telecom sector.

Expansion of Telecom Infrastructure in Emerging Markets to Propel Market Growth

Another significant driver is the expansion of telecom infrastructure in emerging markets. As countries in regions such as Asia-Pacific, Latin America, and Africa experience rapid economic growth, there is a substantial increase in the deployment of telecom networks to meet the rising demand for connectivity. GIS technology is instrumental in these large-scale infrastructure projects, providing essential tools for site selection, route optimization, and infrastructure management. By enabling telecom companies to efficiently plan and deploy their networks in challenging and diverse geographical landscapes, GIS helps accelerate the rollout of telecom services, enhancing connectivity and fostering economic development in these regions.

Restraint Factor for the GIS in Telecom Sector Market

High Initial Investment and Implementation Costs t to Limit the Sales

A significant restraint in the GIS in telecom sector market is the high initial investment and implementation costs. Integrating GIS technology into telecom operations requires substantial financial resources, including the cost of sophisticated software, high-performance hardware, and specialized personnel. Additionally, the implementation process can be complex and time-consuming, often necessitating significant changes to existing workflows and systems. These factors can be particularly challenging for smaller telecom operators and those in emerging markets, potentially hindering the widespread adoption of GIS solutions despite their long-term benefits. This financial barrier remains a critical challenge for the market's growth.

Impact of Covid-19 on the GIS in Telecom Sector Market

The COVID-19 pandemic has had a profound impact on the GIS in telecom sector market, accelerating the adoption of digital solutions and reshaping operational strategies. With the surge in remote work, online education, and digital services, the demand for reliable and expansive telecom networks has skyrocketed. GIS technology has become indispensable for telecom companies in managing this increased demand, aiding in network optimization ...

Welcome to the Empowered Patient Podcast with Karen Jagoda. This show is a window into the latest innovations in digital health and the changing dynamic between doctors and patients.Topics on the show includethe emergence of personalized medicine and breakthroughs in genomicsadvances in biopharmaceuticalsage related diseases and aging in placeusing big data from wearables and sensorstransparency in the medical marketplacechallenges for connected health entrepreneursThe audience includes researchers, medical professionals, patient advocates, entrepreneurs, patients, caregivers, solution providers, students, journalists, and investors._Communities around the world are taking strides in mitigating the threat that COVID-19 (coronavirus) poses. Geography and location analysis have a crucial role in better understanding this evolving pandemic.When you need help quickly, Esri can provide data, software, configurable applications, and technical support for your emergency GIS operations. Use GIS to rapidly access and visualize mission-critical information. Get the information you need quickly, in a way that’s easy to understand, to make better decisions during a crisis.Esri’s Disaster Response Program (DRP) assists with disasters worldwide as part of our corporate citizenship. We support response and relief efforts with GIS technology and expertise.More information...

The Daily Mobility Statistics were derived from a data panel constructed from several mobile data providers, a step taken to address the reduce the risks of geographic and temporal sample bias that would result from using a single data source. In turn, the merged data panel only included data from those mobile devices whose anonymized location data met a set of data quality standards, e.g., temporal frequency and spatial accuracy of anonymized location point observations, device-level temporal coverage and representativeness, spatial distribution of data at the sample and county levels. After this filtering, final mobility estimate statistics were computed using a multi-level weighting method that employed both device- and trip-level weights, thus expanding the sample represented by the devices in the data panel to the at-large populations of each state and county in the US.

Data analysis was conducted at the aggregate national, state, and county levels. To assure confidentiality and support data quality, no data were reported for a county if it had fewer than 50 devices in the sample on any given day.

Trips were defined as movements that included a stay of longer than 10 minutes at an anonymized location away from home. A movement with multiple stays of longer than 10 minutes--before returning home--was counted as multiple trips.

The Daily Mobility Statistics data on this page, which cover the COVID and Post-COVID periods, are experimental. Experimental data products are created using novel or exploratory data sources or methodologies that benefit data users in the absence of other statistically rigorous products, and they not meet all BTS data quality standards.

The Daily Mobility Statistics were derived from a data panel constructed from several mobile data providers, a step taken to address the reduce the risks of geographic and temporal sample bias that would result from using a single data source. In turn, the merged data panel only included data from those mobile devices whose anonymized location data met a set of data quality standards, e.g., temporal frequency and spatial accuracy of anonymized location point observations, device-level temporal coverage and representativeness, spatial distribution of data at the sample and county levels. After this filtering, final mobility estimate statistics were computed using a multi-level weighting method that employed both device- and trip-level weights, thus expanding the sample represented by the devices in the data panel to the at-large populations of each state and county in the US.

Data analysis was conducted at the aggregate national, state, and county levels. To assure confidentiality and support data quality, no data were reported for a county if it had fewer than 50 devices in the sample on any given day.

Trips were defined as movements that included a stay of longer than 10 minutes at an anonymized location away from home. A movement with multiple stays of longer than 10 minutes--before returning home--was counted as multiple trips.

The Daily Mobility Statistics data on this page, which cover the COVID and Post-COVID periods, are experimental. Experimental data products are created using novel or exploratory data sources or methodologies that benefit data users in the absence of other statistically rigorous products, and they not meet all BTS data quality standards.

On March 2, 2022 DC Health announced the District’s new COVID-19 Community Level key metrics and reporting. COVID-19 cases are now reported on a weekly basis. District of Columbia Department of Human Services testing for the number of positive tests, quarantined, returned to work and lives lost. Due to rapidly changing nature of COVID-19, data for March 2020 is limited.General Guidelines for Interpreting Disease Surveillance DataDuring a disease outbreak, the health department will collect, process, and analyze large amounts of information to understand and respond to the health impacts of the disease and its transmission in the community. The sources of disease surveillance information include contact tracing, medical record review, and laboratory information, and are considered protected health information. When interpreting the results of these analyses, it is important to keep in mind that the disease surveillance system may not capture the full picture of the outbreak, and that previously reported data may change over time as it undergoes data quality review or as additional information is added. These analyses, especially within populations with small samples, may be subject to large amounts of variation from day to day. Despite these limitations, data from disease surveillance is a valuable source of information to understand how to stop the spread of COVID19.

BackgroundEarly stages of catastrophes like COVID-19 are often led by chaos and panic. To characterize the initial chaos phase of clinical research in such situations, we analyzed the first surge of more than 1000 clinical trials about the new disease at baseline and after two years follow-up. Our 3 main objectives were: (1) Assessment of spatial and temporal evolution of clinical research of COVID-19 across the globe, (2) Assessment of transparency and quality—trial registration, (3) Assessment of research waste and redundancies.MethodsBy entering the keyword “COVID-19” we screened the International Clinical Trials Registry Platform of the WHO and downloaded the search output when our goal of 1000 trials was reached on the 1st of April 2020. Additionally, we verified the integrity of the downloaded data from the meta registry by comparing the data with each individual registration record on their source register. Also, we conducted a follow-up after two years to track their progress.Results(1) The spatial evolution followed the geographical spread of the disease as expected, however, the temporal development suggested that panic was the main driver for clinical research activities. (2) Trial registrations and registers showed a huge lack of transparency by allowing retrospective registrations and not keeping their registration records up to date. Quality of trial registration seems to have improved over the last decade, yet crucial information still was missing. (3) Research waste and redundancies were present as suggested by discontinuation of trials, preventable flaws in study design, and similar but uncoordinated research topics operationally fragmented in isolated silo-structures.ConclusionThe scientific response mechanism across the globe was intact during the chaos phase. However, supervision, leadership, and accountability are urgently needed to prevent research waste, to ensure effective structure, quality, and validity to ultimately break the “panic-then-forget” cycle in future catastrophes.

The data in this dataset were collected in the result of the survey of Latvian society (2021) aimed at identifying high-value data set for Latvia, i.e. data sets that, in the view of Latvian society, could create the value for the Latvian economy and society. The survey is created for both individuals and businesses. It being made public both to act as supplementary data for "Towards enrichment of the open government data: a stakeholder-centered determination of High-Value Data sets for Latvia" paper (author: Anastasija Nikiforova, University of Latvia) and in order for other researchers to use these data in their own work.

The survey was distributed among Latvian citizens and organisations. The structure of the survey is available in the supplementary file available (see Survey_HighValueDataSets.odt)

Description of the data in this data set: structure of the survey and pre-defined answers (if any) 1. Have you ever used open (government) data? - {(1) yes, once; (2) yes, there has been a little experience; (3) yes, continuously, (4) no, it wasn’t needed for me; (5) no, have tried but has failed} 2. How would you assess the value of open govenment data that are currently available for your personal use or your business? - 5-point Likert scale, where 1 – any to 5 – very high 3. If you ever used the open (government) data, what was the purpose of using them? - {(1) Have not had to use; (2) to identify the situation for an object or ab event (e.g. Covid-19 current state); (3) data-driven decision-making; (4) for the enrichment of my data, i.e. by supplementing them; (5) for better understanding of decisions of the government; (6) awareness of governments’ actions (increasing transparency); (7) forecasting (e.g. trendings etc.); (8) for developing data-driven solutions that use only the open data; (9) for developing data-driven solutions, using open data as a supplement to existing data; (10) for training and education purposes; (11) for entertainment; (12) other (open-ended question) 4. What category(ies) of “high value datasets” is, in you opinion, able to create added value for society or the economy? {(1)Geospatial data; (2) Earth observation and environment; (3) Meteorological; (4) Statistics; (5) Companies and company ownership; (6) Mobility} 5. To what extent do you think the current data catalogue of Latvia’s Open data portal corresponds to the needs of data users/ consumers? - 10-point Likert scale, where 1 – no data are useful, but 10 – fully correspond, i.e. all potentially valuable datasets are available 6. Which of the current data categories in Latvia’s open data portals, in you opinion, most corresponds to the “high value dataset”? - {(1)Foreign affairs; (2) business econonmy; (3) energy; (4) citizens and society; (5) education and sport; (6) culture; (7) regions and municipalities; (8) justice, internal affairs and security; (9) transports; (10) public administration; (11) health; (12) environment; (13) agriculture, food and forestry; (14) science and technologies} 7. Which of them form your TOP-3? - {(1)Foreign affairs; (2) business econonmy; (3) energy; (4) citizens and society; (5) education and sport; (6) culture; (7) regions and municipalities; (8) justice, internal affairs and security; (9) transports; (10) public administration; (11) health; (12) environment; (13) agriculture, food and forestry; (14) science and technologies} 8. How would you assess the value of the following data categories? 8.1. sensor data - 5-point Likert scale, where 1 – not needed to 5 – highly valuable 8.2. real-time data - 5-point Likert scale, where 1 – not needed to 5 – highly valuable 8.3. geospatial data - 5-point Likert scale, where 1 – not needed to 5 – highly valuable 9. What would be these datasets? I.e. what (sub)topic could these data be associated with? - open-ended question 10. Which of the data sets currently available could be valauble and useful for society and businesses? - open-ended question 11. Which of the data sets currently NOT available in Latvia’s open data portal could, in your opinion, be valauble and useful for society and businesses? - open-ended question 12. How did you define them? - {(1)Subjective opinion; (2) experience with data; (3) filtering out the most popular datasets, i.e. basing the on public opinion; (4) other (open-ended question)} 13. How high could be the value of these data sets value for you or your business? - 5-point Likert scale, where 1 – not valuable, 5 – highly valuable 14. Do you represent any company/ organization (are you working anywhere)? (if “yes”, please, fill out the survey twice, i.e. as an individual user AND a company representative) - {yes; no; I am an individual data user; other (open-ended)} 15. What industry/ sector does your company/ organization belong to? (if you do not work at the moment, please, choose the last option) - {Information and communication services; Financial and ansurance activities; Accommodation and catering services; Education; Real estate operations; Wholesale and retail trade; repair of motor vehicles and motorcycles; transport and storage; construction; water supply; waste water; waste management and recovery; electricity, gas supple, heating and air conditioning; manufacturing industry; mining and quarrying; agriculture, forestry and fisheries professional, scientific and technical services; operation of administrative and service services; public administration and defence; compulsory social insurance; health and social care; art, entertainment and recreation; activities of households as employers;; CSO/NGO; Iam not a representative of any company 16. To which category does your company/ organization belong to in terms of its size? - {small; medium; large; self-employeed; I am not a representative of any company} 17. What is the age group that you belong to? (if you are an individual user, not a company representative) - {11..15, 16..20, 21..25, 26..30, 31..35, 36..40, 41..45, 46+, “do not want to reveal”} 18. Please, indicate your education or a scientific degree that corresponds most to you? (if you are an individual user, not a company representative) - {master degree; bachelor’s degree; Dr. and/ or PhD; student (bachelor level); student (master level); doctoral candidate; pupil; do not want to reveal these data}

Format of the file .xls, .csv (for the first spreadsheet only), .odt

Licenses or restrictions CC-BY

The Daily Mobility Statistics were derived from a data panel constructed from several mobile data providers, a step taken to address the reduce the risks of geographic and temporal sample bias that would result from using a single data source. In turn, the merged data panel only included data from those mobile devices whose anonymized location data met a set of data quality standards, e.g., temporal frequency and spatial accuracy of anonymized location point observations, device-level temporal coverage and representativeness, spatial distribution of data at the sample and county levels. After this filtering, final mobility estimate statistics were computed using a multi-level weighting method that employed both device- and trip-level weights, thus expanding the sample represented by the devices in the data panel to the at-large populations of each state and county in the US.

Data analysis was conducted at the aggregate national, state, and county levels. To assure confidentiality and support data quality, no data were reported for a county if it had fewer than 50 devices in the sample on any given day.

Trips were defined as movements that included a stay of longer than 10 minutes at an anonymized location away from home. A movement with multiple stays of longer than 10 minutes--before returning home--was counted as multiple trips.

The Daily Mobility Statistics data on this page, which cover the COVID and Post-COVID periods, are experimental. Experimental data products are created using novel or exploratory data sources or methodologies that benefit data users in the absence of other statistically rigorous products, and they not meet all BTS data quality standards.

This layer shows median household income by race and by age of householder. This is shown by tract, county, and state boundaries. This service is updated annually to contain the most currently released American Community Survey (ACS) 5-year data, and contains estimates and margins of error. There are also additional calculated attributes related to this topic, which can be mapped or used within analysis. Median income and income source is based on income in past 12 months of survey. This layer is symbolized to show median household income. To see the full list of attributes available in this service, go to the "Data" tab, and choose "Fields" at the top right. Current Vintage: 2019-2023ACS Table(s): B19013B, B19013C, B19013D, B19013E, B19013F, B19013G, B19013H, B19013I, B19049, B19053Data downloaded from: Census Bureau's API for American Community Survey Date of API call: December 12, 2024National Figures: data.census.govThe United States Census Bureau's American Community Survey (ACS):About the SurveyGeography & ACSTechnical DocumentationNews & UpdatesThis ready-to-use layer can be used within ArcGIS Pro, ArcGIS Online, its configurable apps, dashboards, Story Maps, custom apps, and mobile apps. Data can also be exported for offline workflows. For more information about ACS layers, visit the FAQ. Please cite the Census and ACS when using this data.Data Note from the Census:Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see Accuracy of the Data). The effect of nonsampling error is not represented in these tables.Data Processing Notes:This layer is updated automatically when the most current vintage of ACS data is released each year, usually in December. The layer always contains the latest available ACS 5-year estimates. It is updated annually within days of the Census Bureau's release schedule. Click here to learn more about ACS data releases.Boundaries come from the US Census TIGER geodatabases, specifically, the National Sub-State Geography Database (named tlgdb_(year)_a_us_substategeo.gdb). Boundaries are updated at the same time as the data updates (annually), and the boundary vintage appropriately matches the data vintage as specified by the Census. These are Census boundaries with water and/or coastlines erased for cartographic and mapping purposes. For census tracts, the water cutouts are derived from a subset of the 2020 Areal Hydrography boundaries offered by TIGER. Water bodies and rivers which are 50 million square meters or larger (mid to large sized water bodies) are erased from the tract level boundaries, as well as additional important features. For state and county boundaries, the water and coastlines are derived from the coastlines of the 2023 500k TIGER Cartographic Boundary Shapefiles. These are erased to more accurately portray the coastlines and Great Lakes. The original AWATER and ALAND fields are still available as attributes within the data table (units are square meters).The States layer contains 52 records - all US states, Washington D.C., and Puerto RicoCensus tracts with no population that occur in areas of water, such as oceans, are removed from this data service (Census Tracts beginning with 99).Percentages and derived counts, and associated margins of error, are calculated values (that can be identified by the "_calc_" stub in the field name), and abide by the specifications defined by the American Community Survey.Field alias names were created based on the Table Shells file available from the American Community Survey Summary File Documentation page.Negative values (e.g., -4444...) have been set to null, with the exception of -5555... which has been set to zero. These negative values exist in the raw API data to indicate the following situations:The margin of error column indicates that either no sample observations or too few sample observations were available to compute a standard error and thus the margin of error. A statistical test is not appropriate.Either no sample observations or too few sample observations were available to compute an estimate, or a ratio of medians cannot be calculated because one or both of the median estimates falls in the lowest interval or upper interval of an open-ended distribution.The median falls in the lowest interval of an open-ended distribution, or in the upper interval of an open-ended distribution. A statistical test is not appropriate.The estimate is controlled. A statistical test for sampling variability is not appropriate.The data for this geographic area cannot be displayed because the number of sample cases is too small.

On March 2, 2022 DC Health announced the District’s new COVID-19 Community Level key metrics and reporting. COVID-19 cases are now reported on a weekly basis. More information available at https://coronavirus.dc.gov. District of Columbia Department of Correction, both personnel and resident, testing for the number of positive tests, quarantined, returned to work, recovery and lives lost. Due to rapidly changing nature of COVID-19, data for March 2020 is limited.General Guidelines for Interpreting Disease Surveillance DataDuring a disease outbreak, the health department will collect, process, and analyze large amounts of information to understand and respond to the health impacts of the disease and its transmission in the community. The sources of disease surveillance information include contact tracing, medical record review, and laboratory information, and are considered protected health information. When interpreting the results of these analyses, it is important to keep in mind that the disease surveillance system may not capture the full picture of the outbreak, and that previously reported data may change over time as it undergoes data quality review or as additional information is added. These analyses, especially within populations with small samples, may be subject to large amounts of variation from day to day. Despite these limitations, data from disease surveillance is a valuable source of information to understand how to stop the spread of COVID19.

Coronavirus disease 2019 (COVID-19) has brought dramatic changes in our daily life, especially in human mobility since 2020. As the major component of the integrated transport system in most cities, taxi trips represent a large portion of residents’ urban mobility. Thus, quantifying the impacts of COVID-19 on city-wide taxi demand can help to better understand the reshaped travel patterns, optimize public-transport operational strategies, and gather emergency experience under the pressure of this pandemic. To achieve the objectives, the Geographically and Temporally Weighted Regression (GTWR) model is used to analyze the impact mechanism of COVID-19 on taxi demand in this study. City-wide taxi trip data from August 1st, 2020 to July 31st, 2021 in New York City was collected as model’s dependent variables, and COVID-19 case rate, population density, road density, station density, points of interest (POI) were selected as the independent variables. By comparing GTWR model with traditional ordinary least square (OLS) model, temporally weighted regression model (TWR) and geographically weighted regression (GWR) model, a significantly better goodness of fit on spatial-temporal taxi data was observed for GTWR. Furthermore, temporal analysis, spatial analysis and the epidemic marginal effect were developed on the GTWR model results. The conclusions of this research are shown as follows: (1) The virus and health care become the major restraining and stimulative factors of taxi demand in post epidemic era. (2) The restraining level of COVID-19 on taxi demand is higher in cold weather. (3) The restraining level of COVID-19 on taxi demand is severely influenced by the curfew policy. (4) Although this virus decreases taxi demand in most of time and places, it can still increase taxi demand in some specific time and places. (5) Along with COVID-19, sports facilities and tourism become obstacles on increasing taxi demand in most of places and time in post epidemic era. The findings can provide useful insights for policymakers and stakeholders to improve the taxi operational efficiency during the remainder of the COVID-19 pandemic.

Coronavirus disease 2019 (COVID-19) has brought dramatic changes in our daily life, especially in human mobility since 2020. As the major component of the integrated transport system in most cities, taxi trips represent a large portion of residents’ urban mobility. Thus, quantifying the impacts of COVID-19 on city-wide taxi demand can help to better understand the reshaped travel patterns, optimize public-transport operational strategies, and gather emergency experience under the pressure of this pandemic. To achieve the objectives, the Geographically and Temporally Weighted Regression (GTWR) model is used to analyze the impact mechanism of COVID-19 on taxi demand in this study. City-wide taxi trip data from August 1st, 2020 to July 31st, 2021 in New York City was collected as model’s dependent variables, and COVID-19 case rate, population density, road density, station density, points of interest (POI) were selected as the independent variables. By comparing GTWR model with traditional ordinary least square (OLS) model, temporally weighted regression model (TWR) and geographically weighted regression (GWR) model, a significantly better goodness of fit on spatial-temporal taxi data was observed for GTWR. Furthermore, temporal analysis, spatial analysis and the epidemic marginal effect were developed on the GTWR model results. The conclusions of this research are shown as follows: (1) The virus and health care become the major restraining and stimulative factors of taxi demand in post epidemic era. (2) The restraining level of COVID-19 on taxi demand is higher in cold weather. (3) The restraining level of COVID-19 on taxi demand is severely influenced by the curfew policy. (4) Although this virus decreases taxi demand in most of time and places, it can still increase taxi demand in some specific time and places. (5) Along with COVID-19, sports facilities and tourism become obstacles on increasing taxi demand in most of places and time in post epidemic era. The findings can provide useful insights for policymakers and stakeholders to improve the taxi operational efficiency during the remainder of the COVID-19 pandemic.