Mobile LiDAR Mapping Service (Civil Works) Market Outlook

According to our latest research, the global Mobile LiDAR Mapping Service (Civil Works) market size reached USD 2.15 billion in 2024, driven by the growing demand for high-precision geospatial data in infrastructure projects. The market is expected to expand at a robust CAGR of 13.7% during the forecast period, reaching a projected value of USD 6.01 billion by 2033. This growth is fueled by the rapid adoption of advanced mapping technologies across civil engineering, urban planning, and infrastructure maintenance sectors, as organizations increasingly recognize the efficiency, accuracy, and cost-effectiveness of Mobile LiDAR for large-scale civil works applications.

One of the primary growth factors for the Mobile LiDAR Mapping Service (Civil Works) market is the surging demand for precise and real-time geospatial data in the construction and maintenance of transportation infrastructure. As roadways, bridges, and railways undergo expansion and modernization globally, stakeholders are turning to Mobile LiDAR solutions for topographic mapping, corridor analysis, and asset management. The ability of Mobile LiDAR to quickly and safely collect highly accurate three-dimensional data, even in challenging or inaccessible environments, is transforming how civil works projects are planned, executed, and monitored. This not only accelerates project timelines but also reduces costs associated with manual surveying and minimizes risks to personnel, further driving market adoption.

Another significant growth driver is the integration of Mobile LiDAR mapping with digital twin technologies and Building Information Modeling (BIM). As governments and private enterprises invest in smart city initiatives and infrastructure digitalization, the need for comprehensive, up-to-date, and interoperable geospatial data has never been greater. Mobile LiDAR mapping services provide the foundational datasets required for creating accurate digital replicas of physical assets, enabling predictive maintenance, enhanced asset management, and data-driven urban planning. This convergence of LiDAR with advanced analytics and visualization platforms is opening new avenues for service providers, while also pushing the boundaries of what is possible in civil works engineering and management.

Environmental regulations and the growing emphasis on sustainability in infrastructure development are also propelling the Mobile LiDAR Mapping Service (Civil Works) market forward. Regulatory authorities increasingly mandate detailed environmental impact assessments and ongoing monitoring for large-scale projects. Mobile LiDAR’s ability to deliver high-resolution terrain and vegetation data supports compliance, biodiversity management, and the minimization of ecological footprints. Furthermore, the technology’s non-intrusive nature ensures minimal disruption to natural habitats during data collection, aligning with global sustainability goals. Together, these factors are making Mobile LiDAR mapping indispensable for environmentally conscious civil engineering projects.

Regionally, North America continues to dominate the Mobile LiDAR Mapping Service (Civil Works) market, accounting for the largest revenue share in 2024, followed closely by Europe and Asia Pacific. The United States leads in technology adoption, thanks to substantial investments in infrastructure modernization and a mature ecosystem of LiDAR service providers. Meanwhile, Asia Pacific is witnessing the fastest growth, buoyed by massive infrastructure development in China, India, and Southeast Asia, as well as increasing government initiatives supporting smart cities and digital mapping. Europe’s growth is underpinned by stringent environmental regulations and a strong focus on transportation safety and modernization. Latin America and the Middle East & Africa are emerging markets, gradually increasing their adoption rates as awareness and investment in advanced civil works technologies rise.

Service Type Analysis

Cloud-Based Mapping Service Market Outlook

The global cloud-based mapping service market size was valued at approximately USD 3.5 billion in 2023 and is projected to reach around USD 8.9 billion by 2032, exhibiting a compound annual growth rate (CAGR) of 11.2% during the forecast period. This remarkable growth is primarily driven by the increasing demand for real-time data access and navigation services across various sectors. Businesses and governments worldwide are increasingly leveraging cloud-based mapping services to optimize operations, improve customer experience, and enhance decision-making processes. The seamless integration of advanced technologies such as Artificial Intelligence (AI) and Internet of Things (IoT) in mapping services is further boosting this market's expansion.

The integration of AI with cloud-based mapping services is one of the key growth factors for this market. AI technologies enhance the capabilities of cloud-based mapping services by providing intelligent insights and predictive analytics. For instance, AI can analyze traffic patterns and predict congestion, offering alternative routes and optimal travel paths. This is particularly beneficial for the transportation and logistics sectors, where time is of the essence. Furthermore, AI-driven mapping services can assist businesses in understanding consumer behavior and preferences, allowing for targeted marketing strategies and improved customer engagement. The ability of AI to process massive datasets quickly and accurately makes it a valuable tool in the cloud-based mapping service industry.

Another significant factor contributing to market growth is the rising adoption of IoT devices. IoT devices generate a vast amount of location-based data that can be effectively managed and utilized through cloud-based mapping services. These services enable businesses to track and monitor assets, vehicles, and personnel in real-time, leading to improved operational efficiency and reduced costs. For example, in the logistics sector, companies can use cloud-based mapping services to optimize delivery routes and monitor vehicle conditions, thereby minimizing fuel consumption and enhancing customer satisfaction. The continuous evolution and proliferation of IoT devices are expected to drive further demand for cloud-based mapping services in the coming years.

The increasing reliance on mobile devices and the proliferation of high-speed internet connectivity are also significant growth drivers for the cloud-based mapping service market. With the widespread use of smartphones and tablets, consumers and businesses alike are accessing mapping services on-the-go, necessitating reliable cloud-based solutions. The availability of high-speed internet ensures seamless connectivity and real-time updates, enhancing user experience. This trend is particularly prominent in urban areas, where demand for navigation and location-based services is high. As mobile technology continues to evolve and internet infrastructure improves worldwide, the cloud-based mapping service market is poised for substantial growth.

The rise of URL Shortening Services has become increasingly relevant in the context of cloud-based mapping services. These services allow users to condense lengthy URLs into shorter, more manageable links, which is particularly useful for sharing location-based information. In industries such as logistics and transportation, where quick access to precise location data is crucial, URL shortening can streamline communication and improve efficiency. By integrating URL shortening with mapping services, businesses can enhance their digital marketing strategies and facilitate easier sharing of maps and navigation routes. This integration not only improves user experience but also supports the growing demand for seamless digital interactions in the mapping service market.

Service Type Analysis

The cloud-based mapping service market is segmented into several service types, each offering unique features and benefits to users. Mapping and navigation services are perhaps the most widely recognized and utilized among these. They provide users with detailed maps, directions, and navigation assistance, which are crucial for both consumers and businesses. These services cater to a wide array of applications, from personal navigation to complex logistics operations. As the demand for precise, real-time navigation grows, mapping and navigation services continue to be at the forefront of the cloud-based mapping industry. Their integrat

Work in progress: data might be changed The data set contains the locations of public roadside parking spaces in the northeastern part of Hanover Linden-Nord. As a sample data set, it explicitly does not provide a complete, accurate or correct representation of the conditions! It was collected and processed as part of the 5GAPS research project on September 22nd and October 6th 2022 as a basis for further analysis and in particular as input for simulation studies. Vehicle Detections Based on the mapping methodology of Bock et al. (2015) and processing of Leichter et al. (2021), the utilization was determined using vehicle detections in segmented 3D point clouds. The corresponding point clouds were collected by driving over the area on two half-days using a LiDAR mobile mapping system, resulting in several hours between observations. Accordingly, these are only a few sample observations. The trips are made in such a way that combined they cover a synthetic day from about 8-20 clock. The collected point clouds were georeferenced, processed, and automatically segmented semantically (see Leichter et al., 2021). To automatically extract cars, those points with car labels were clustered by observation epoch and bounding boxes were estimated for the clusters as a representation of car instances. The boxes serve both to filter out unrealistically small and large objects, and to rudimentarily complete the vehicle footprint that may not be fully captured from all sides. Figure 1: Overview map of detected vehicles Parking Areas

Description Cellphone tower extract is a copy of the data found at https://ised-isde.canada.ca/site/spectrum-management-system/en/spectrum-management-system-data. Data is organized into a point layer of tower locations grouped by provider. All the providers transmitters are located in a table related by a unique TowerID.Dataset Usage General data layer for use when needed, for example to identify shortfalls in cell service for field work.Data Source Modified version of Innovation, Science and Economic Development Canada datasetData Criticality 1Sensitive Data NoCurator Greg SpiridonovCurator Job Title GIS SpecialistCurator Email greg.spiridonov@grey.caCurator Department IT / GISCurator Responsibilities Maintain,Oversight_Control_AccessMaintenance and Update Frequency MonthlyUpdate History New ZIP downloaded Nov 14 2023Published Map Service(s) https://gis.grey.ca/portal/home/item.html?id=718f08a731924856827f85178bb649cbPublicly Available Publicly availableOpen Data Published to Open DataOffline (sync) Not sure at this timeOther Comments Dataset relates to table GC_CellTower_TransmittersPermissionsAssign permissions to map service if published.Group PermissionsCurator Department ViewGrey County Staff ViewPublic View

The i.c.sens Visual-Inertial-LiDAR Dataset is a data set for the evaluation of dead reckoning or SLAM approaches in the context of mobile robotics. It consists of street-level monocular RGB camera images, a front-facing 180° point cloud, angular velocities, accelerations and an accurate ground truth trajectory. In total, we provide around 77 GB of data resulting from a 15 minutes drive, which is split into 8 rosbags of 2 minutes (10 GB) each. Besides, the intrinsic camera parameters and the extrinsic transformations between all sensor coordinate systems are given. Details on the data and its usage can be found in the provided documentation file. Image credit: Sören Vogel The data set was acquired in the context of the measurement campaign described in Schoen2018. Here, a vehicle, which can be seen below, was equipped with a self-developed sensor platform and a commercially available Riegl VMX-250 Mobile Mapping System. This Mobile Mapping System consists of two laser scanners, a camera system and a localization unit containing a highly accurate GNSS/IMU system. Image credit: Sören Vogel The data acquisition took place in May 2019 during a sunny day in the Nordstadt of Hannover (coordinates: 52.388598, 9.716389). The route we took can be seen below. This route was completed three times in total, which amounts to a total driving time of 15 minutes. The self-developed sensor platform consists of several sensors. This dataset provides data from the following sensors: Velodyne HDL-64 LiDAR LORD MicroStrain 3DM-GQ4-45 GNSS aided IMU Pointgrey GS3-U3-23S6C-C RGB camera To inspect the data, first start a rosmaster and launch rviz using the provided configuration file: roscore & rosrun rviz rviz -d icsens_data.rviz

A real-world multi-vehicle multi-modal V2V and V2X dataset Recently published datasets have been increasingly comprehensive with respect to their variety of simultaneously used sensors, traffic scenarios, environmental conditions, and provided annotations. However, these datasets typically only consider data collected by one independent vehicle. Hence, there is currently a lack of comprehensive, real-world, multi-vehicle datasets fostering research on cooperative applications such as object detection, urban navigation, or multi-agent SLAM. In this paper, we aim to fill this gap by introducing the novel LUCOOP dataset, which provides time-synchronized multi-modal data collected by three interacting measurement vehicles. The driving scenario corresponds to a follow-up setup of multiple rounds in an inner city triangular trajectory. Each vehicle was equipped with a broad sensor suite including at least one LiDAR sensor, one GNSS antenna, and up to three IMUs. Additionally, Ultra-Wide-Band (UWB) sensors were mounted on each vehicle, as well as statically placed along the trajectory enabling both V2V and V2X range measurements. Furthermore, a part of the trajectory was monitored by a total station resulting in a highly accurate reference trajectory. The LUCOOP dataset also includes a precise, dense 3D map point cloud, acquired simultaneously by a mobile mapping system, as well as an LOD2 city model of the measurement area. We provide sensor measurements in a multi-vehicle setup for a trajectory of more than 4 km and a time interval of more than 26 minutes, respectively. Overall, our dataset includes more than 54,000 LiDAR frames, approximately 700,000 IMU measurements, and more than 2.5 hours of 10 Hz GNSS raw measurements along with 1 Hz data from a reference station. Furthermore, we provide more than 6,000 total station measurements over a trajectory of more than 1 km and 1,874 V2V and 267 V2X UWB measurements. Additionally, we offer 3D bounding box annotations for evaluating object detection approaches, as well as highly accurate ground truth poses for each vehicle throughout the measurement campaign. Data access Important: Before downloading and using the data, please check the Updates.zip in the "Data and Resources" section at the bottom of this web site. There, you find updated files and annotations as well as update notes. The dataset is available here. Additional information are provided and constantly updated in our README. The corresponding paper is available here. Cite this as: J. Axmann et al., "LUCOOP: Leibniz University Cooperative Perception and Urban Navigation Dataset," 2023 IEEE Intelligent Vehicles Symposium (IV), Anchorage, AK, USA, 2023, pp. 1-8, doi: 10.1109/IV55152.2023.10186693.

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This map presents transportation data, including highways, roads, railroads, and airports for the world.

The map was developed by Esri using Esri highway data; Garmin basemap layers; HERE street data for North America, Europe, Australia, New Zealand, South America and Central America, India, most of the Middle East and Asia, and select countries in Africa. Data for Pacific Island nations and the remaining countries of Africa was sourced from OpenStreetMap contributors. Specific country list and documentation of Esri's process for including OSM data is available to view.

These data were automated to provide an accurate high-resolution composite shoreline of GRAND BAY TO PENSACOLA MOBILE BAY, AL suitable as a geographic information system (GIS) data layer. These data are derived from shoreline maps that were produced by the NOAA National Ocean Service including its predecessor agencies. This metadata describes information for both the line and point shapefiles. The NGS's attribution scheme 'Coastal Cartographic Object Attribute Source Table (C-COAST) was developed to conform the attribution of various sources of shoreline data into one attribution catalog. C-COAST is not a recognized standard but was influenced by the International Hydrographic Organization's S-57 Object-Attribute standard so that the data would be more accurately translated into S-57.

This dataset represents the Land Mobile Broadcast tower locations as recorded by the Federal Communications Commission. Serve as base information for use in GIS systems for general planning, analytical, and research purposes. It is not intended for engineering work or to legally define FCC licensee data or FCC market boundaries. The material in these data and text files are provided as-is. The FCC disclaims all warranties with regard to the contents of these files, including their fitness. In no event shall the FCC be liable for any special, indirect, or consequential damages whatsoever resulting from loss or use, data or profits, whether in connection with the use or performance of the contents of these files, action of contract, negligence, or other action arising out of, or in connection with the use of the contents of these files. It is know that there are some errors in the licensing information - Latitude, Longitude and Ground Elevation data as well as frequency assignment data from which these files were generated.

TUM-MLS-2016: An annotated mobile lidar dataset of the TUM City Campus for semantic point cloud interpretation in urban areas.

The following data set contains service request activity for Miami-Dade County. The data sets include services completed proactively by Miami-Dade County departments and requests submitted by citizens via phone (311), online (miamidade.gov), and other self service channels such as the 311Direct mobile application. With a few exceptions, the dataset does not generally include requests from other cities (City of Miami, Coral Gables, etc.) unless the work is owned by Miami-Dade County staff. Case Owner is (match ANY condition): Animal_Services or Citations_and_Tags or Enforcement_Section-3-36

This database presents the results of the MOVE Project Survey (Work Package 4) that has received funding from the European Union’s Horizon 2020 research and innovation programme under Grant Agreement No. 649263. The consortium of MOVE comprises nine partners in six countries: Luxembourg, Germany, Hungary, Norway, Romania, and Spain. The central aim of MOVE is to provide evidence-based knowledge on mobility of young people in Europe as a prerequisite to improve mobility conditions, and to identify fostering and hindering factors of “beneficial” mobility. This aim is pursued using a multilevel interdisciplinary research approach, aiming at a comprehensive and systematic analysis of the mobility of young people in Europe. Objectives of the Survey: –To find out about the role and value of information and support services for young people and their decision making process to go abroad. –To explore the role of transnational networks for support and as a potential “pull factor” for mobility. –To examine the agency of young people with mobility experience and without it. –To study the formation of social capital and the dimensions of social inequality of mobile young people and their effects on future perspectives as well as the reproduction of social inequalities. –To carry out research on the formation of identity by those mobile young people compared to non- mobile ones. –To examine the career-plans of young people and their personal attachments related to their commitments in their home country (e.g. sending money home, supporting the family, etc.) –To gain insights into the (re)production of social inequality concerning mobility and non- mobility. Combined online panel and snowball survey. The Online Panel Survey Design and Field Research. Universe: Mobile and non-mobile young people between 18 and 29 years of age, nationals of at least one of the consortium partner, or those who obtained the secondary school certificate/diploma in any of the six participating countries. Sample error: n=1,000 interviews, +/- 3.2%; n=750 interviews +/- 3.7% confidence inter- val 95%. Quality standards: ISOMAR, ISO, AENOR, IQNet. Sample size: 5,769 questionnaires. Languages: The online survey was available in, French, German, Hungarian, Norwegian (Nynorsk and Bokm˚al), Luxembourgish, German for Luxembourg, Romanian and Spanish. Fieldwork dates: 23rd of November 2016 to 30th of January 2017, accounting for 8 weeks. Pre-test: The questionnaire was submitted to a pre-test, and amendments were introduced to improve the final results. The Online Snowball Survey The online survey panel was complemented with a snowball sampling, self-selected, online survey targeting only young people involved, in the past or currently, in a mobility process (n=3,207). Furthermore, as presented in D.4.4, snowball sampling (Goodman 1961), is the most efficient way to obtain respondents through referrals amongst people sharing the same features, which includes hidden populations amongst migrants. Design and Field Research The questionnaire design process followed the same work flow as the online panel survey questionnaire, using the same set of questions, except those related to the non-mobile questions which were deleted. The survey design and field research were unfolded as follows: Universe: people living abroad or people with mobility experience between 18 and 29 years of age. Nationals from one of the participating countries or those who obtained the secondary school certificate/diploma in any of the six participating countries. Methodology: non-probabilistic snowball Sample size: n=3,207. Languages: French, German, Hungarian, Norwegian (Nynorsk and Bokm˚al), Luxembourgish, German for Luxembourg, Romanian and Spanish. Duration: 15 to 25 minutes. Fieldwork dates: 7th of December 2016, reaching peak activity from 19th of December 2016 to 31st of January 2017, and finished on 5th of February 2017. Sample per country: A questionnaire was assigned to a consortium country whenever the respondent was a national, had obtained his/her secondary school certificate or had carried out the last year of studies before dropping out in the said country.

This data aims to analyze the intellectual structure of mobile money studies by examining bibliographic characteristics. A dataset of 165 documents from the Scopus database was used. This study explored various aspects, including annual publication counts, country coupling, source numbers, primary research areas, co-occurrence of keywords, bibliographic linkages between sources and documents, and co-citation patterns of references. Bibliographic network mapping techniques were applied to analyze the data. The analysis was performed using VOSviewer, a scientific mapping tool. The results showed four main themes of the mobile money dataset: mobile money in Africa, financial inclusion, electronic money, and digital financial services.

Grade PT2 easements concern easements for the protection of radio transmission and reception centres against obstacles. They are established pursuant to Articles L. 54 to L.56-1 of the Postal and Electronic Communications Code in order to protect radio centres from physical obstacles that may impede the spread of waves. Two schemes should be distinguished: — easements established for the benefit of radio centres concerning national defence or public security (Articles L.54 to L.56 of the Postal and Electronic Communications Code); — easements for radio centres owned by private operators (Article L.56-1 of the Postal and Electronic Communications Code). However, in the absence of a decree implementing Article L.62-1 of the Postal and Electronic Communications Code, operators of electronic communications networks open to the public cannot benefit from radio easements to date. A plan for the establishment of easements approved by decree sets out the areas that are subject to easement. Four types of zone can be created: — primary clearance zones and/or secondary clearance zones around each radio station emitting or receiving radio waves using directional aerials, as well as around radio laboratories and research centres; — special clearance zones between two centres providing a radio wave link greater than 30 megahertz (i.e. with a wavelength of less than 10 metres); — areas of clearance around radio-tracking stations or radionavigation stations of transmission or reception. The consequence of servitude is: — the obligation, in all those areas, for owners to carry out, if necessary, the removal or modification of buildings constituting buildings by nature pursuant to Articles 518 and 519 of the Civil Code. In the absence of an amicable agreement, the administration may expropriate these properties; — the prohibition, in all these areas, of creating fixed or mobile obstacles the highest part of which exceeds the ratings fixed by the easement order without the authorisation of the minister who operates or controls the centre; — prohibition, in the primary clearance zone: * an aeronautical safety station or radiogoniometric centre, to create or retain any fixed or mobile metalwork, bodies of water or liquids of any kind that may interfere with the operation of that facility or station; * an aeronautical safety station, to create or maintain artificial excavations that may interfere with the operation of that station. * the prohibition, in the special clearance zone, of creating structures or obstacles siuted above a straight line 10 metres below that joining the emission and receiving airplanes, but the height limitation imposed on a construction may not be less than 25 metres. This resource describes the surface bases of the PT2 class easements, i.e. the areas (primary, secondary, special) and clearance areas Source: —NR— Vintage: —NR— Dissemination: Restricted

This dataset is the first 1: 100,000 desert spatial database in China based on the graphic data of desert thematic maps. It mainly reflects the geographical distribution, area size, and mobility of sand dunes in China. According to the system design requirements and relevant standards, the input data is standardized and uniformly converted into a standard format for various types of data input. Build a library to run the delivery system. This project uses the TM image in 2000 as the information source, and interprets, extracts, and edits the coverage of the national land use map and TM digital image information in 2000. It uses remote sensing and geographic information system technology to 1: 100,000 Thematic mapping requirements for scale bar maps were made on the desert, sandy land and gravel Gobi in China. The 1: 100,000 desert map across the country can save users a lot of data entry and editing work when they are engaged in research on resources and the environment. Digital maps can be easily converted into layout maps The dataset properties are as follows: Divided into two folders e00 and shp: Desert map name and province comparison table in each folder 01 Ahsm Anhui 02 Bjsm Beijing 03 Fjsm Fujian 04 Gdsm Guangdong 05 Gssm Gansu 06 Gxsm Guangxi Zhuang Autonomous Region 07 Gzsm Guizhou 08 Hebsm Hebei 09 Hensm Henan 10 Hljsm Heilongjiang 11 Hndsm Hainan 12 Hubsm Hubei 13 Jlsm Jilin Province 14 Jssm Jiangsu 15 Jxsm Jiangxi 16 Lnsm Liaoning 17 Nmsm Inner Mongolia Gu Autonomous Region 18 Nxsm Ningxia Hui Autonomous Region 19 Qhsm Qinghai 20 Scsm Sichuan 21 Sdsm Shandong 22 Sxsm Shaanxi Province 23 Tjsm Tianjin 24 Twsm Taiwan Province 25 Xjsm Xinjiang Uygur Autonomous Region 26 Xzsm Tibet Autonomous Region 27 Zjsm Zhejiang 28 Shxsm Shanxi 1. Data projection: Projection: Albers False_Easting: 0.000000 False_Northing: 0.000000 Central_Meridian: 105.000000 Standard_Parallel_1: 25.000000 Standard_Parallel_2: 47.000000 Latitude_Of_Origin: 0.000000 Linear Unit: Meter (1.000000) 2. Data attribute table: area (area) perimeter ashm_ (sequence code) class (desert encoding) ashm_id (desert encoding) 3. Desert coding: mobile sandy land 2341010 Semi-mobile sandy land Semi-fixed sandy land 2341030 Gobi 2342000 Saline land 2343000 4: File format: National, sub-provincial and county-level desert map data types are vector shapefiles and E00 5: File naming: Data organization based on the National Basic Resources and Environmental Remote Sensing Dynamic Information Service System is performed on the file management layer of Windows NT. The file and directory names are compound names of English characters and numbers. Pinyin + SM composition, such as the desert map of Gansu Province is GSSM. The flag and county desert map is the pinyin + xxxx of the province name, and xxxx is the last four digits of the flag and county code. The division of provinces, districts, flags and counties is based on the administrative division data files in the national basic resources and environmental remote sensing dynamic information service operation system.

Vegetation structure metrics of 127 LA plots, derived from mobile lidar scans (GeoSLAM ZEB Horizon). Metrics cover information on structural complexity, vegetation height, canopy surface, vertical layering, vegetation cover and gaps.

Retained-Earnings Time Series for Beijing SuperMap Software Co Ltd. Beijing SuperMap Software Co., Ltd. provides geographic information system and spatial intelligence software products and services in China and internationally. The company offers SuperMap iServer, which provides web services for geospatial big data; GeoAI, an 3D to support massive vector/raster data publishing; SuperMap iPortal that offers Web applications; and SuperMap iManager to monitor various GIS data storage, computing, service nodes, or other Web sites, as well as occupancy of hardware resources, map access hotspots, node health, and other indicators to achieve integrated operation and maintenance management of GIS system. It also provides Edge GIS Server for service publishing and real-time analysis and calculation, reduces response latency and bandwidth consumption, and reduces the pressure of cloud GIS center; Terminal GIS for Components, a large-scale full-component GIS development platform; SuperMap iDesktop and SuperMap iDesktopX, which are 2D and 3D integrated desktop GIS software platforms; SuperMap iExplorer3D, a 3D scene browsing software; SuperMap iMaritimeEditor, a cross-platform electronic chart production desktop software; and SuperMap ImageX Pro, a cross-platform remote sensing image processing desktop software. In addition, the company offers SuperMap iClient JavaScript, a GIS web terminal development platform; SuperMap iClient3D for WebGL, a 3D web terminal development platform; SuperMap iClient3D for WebGPU, a 3D GIS network client development platform; SuperMap iMobile, a mobile GIS software development platform based on map browsing, data collection, data analysis, and route navigation and combined with AR maps, mobile 3D, cloud collaboration, etc.; and SuperMap online GIS platform that integrates GIS data management, service management, data mining, and display. Beijing SuperMap Software Co., Ltd. was founded in 1997 and is based in Beijing, the People's Republic of China.

Investments Time Series for Beijing SuperMap Software Co Ltd. Beijing SuperMap Software Co., Ltd. provides geographic information system and spatial intelligence software products and services in China and internationally. The company offers SuperMap iServer, which provides web services for geospatial big data; GeoAI, an 3D to support massive vector/raster data publishing; SuperMap iPortal that offers Web applications; and SuperMap iManager to monitor various GIS data storage, computing, service nodes, or other Web sites, as well as occupancy of hardware resources, map access hotspots, node health, and other indicators to achieve integrated operation and maintenance management of GIS system. It also provides Edge GIS Server for service publishing and real-time analysis and calculation, reduces response latency and bandwidth consumption, and reduces the pressure of cloud GIS center; Terminal GIS for Components, a large-scale full-component GIS development platform; SuperMap iDesktop and SuperMap iDesktopX, which are 2D and 3D integrated desktop GIS software platforms; SuperMap iExplorer3D, a 3D scene browsing software; SuperMap iMaritimeEditor, a cross-platform electronic chart production desktop software; and SuperMap ImageX Pro, a cross-platform remote sensing image processing desktop software. In addition, the company offers SuperMap iClient JavaScript, a GIS web terminal development platform; SuperMap iClient3D for WebGL, a 3D web terminal development platform; SuperMap iClient3D for WebGPU, a 3D GIS network client development platform; SuperMap iMobile, a mobile GIS software development platform based on map browsing, data collection, data analysis, and route navigation and combined with AR maps, mobile 3D, cloud collaboration, etc.; and SuperMap online GIS platform that integrates GIS data management, service management, data mining, and display. Beijing SuperMap Software Co., Ltd. was founded in 1997 and is based in Beijing, the People's Republic of China.

Using GPS on phones and a mobile website questionnaire, survey participants check-in at every location visited for one week. Participants also complete a KeySurvey survey about their demographics, health, and local area. Using GIS (Google Earth) and Excel, the check-in data is analysed and presented in the form of images and Word document profiles of movements for each participant. Further work will involve Excel or SPSS and GIS analysis of the entire dataset. This research is part of the project 'Using Mobile Locative Media to Map Opportunities for Lifestyle Change in Women with Cancer Risk Factors: Healthy Mobile Check-Ins.'