This layer was created to provide an authoritative database of statewide trails, to be used primarily for planning and cartographic purposes, and to provide a model to encourage consistency in the mapping of trails and trail attributes for the maintenance and update of accurate trail data. NOTE: This dataset is a first iteration and is in no way complete; instead, this should be viewed as a ‘living’ dataset. The NJ Trails Taskforce is currently developing a process to maintain, fill in data gaps, and make the dataset more robust over time There is no authoritative central location for the public to access Trail locations online. Many independently owned websites offer this information, but it is often crowd sourced and therefore may not be accurate. If the state hosted and maintained the NJ Statewide Trails GIS data then the general public, landowners, land managers, and first responders would have a reliable place to look for this information. Data can be used for planning new trails, advocating for use, search and rescue, and more. NOTE: Managing agency will still be primary point of contact for the most accurate, up-to-date trail locations and conditions. Users of the data will be able to see where trails are in relation to their area of interest. Users can use trail data to plan hiking trips and create routes. Data can be used on maps to show location of trails when relevant or used in analysis. First responders can use data to find lost hikers. There is also a benefit statewide – data can be used as a statewide trail planning tool to improve New Jersey’s trail network. For Green Acres and others involved in land preservation, this data will assist in the identification of parcel acquisitions needed to connect larger trail systems, and it will allow the Trails Program to effectively evaluate project proposals. For the Office of Environmental Justice, the environmental justice mapping tool can be used in conjunction with this database to target efforts in overburdened communities where trails or access to trails are limited or nonexistent. For the State Park Service, this data will allow land managers to make trail connections to adjacent properties to improve the trail network, and the data can be used on existing maps to show trails that neighbor park properties. For the New Jersey State Police, Search and Rescue efforts can be aided using this data. For government agencies, non-profits and others, trail data will provide the data necessary to accurately plan for connections or extensions to existing trail networks and/or be used to advocate for the creation of new and/or connecting trail networks.

Introduction

This travel time matrix records travel times and travel distances for routes between all centroids (N = 13132) of a 250 × 250 m grid over the populated areas in the Helsinki metropolitan area by walking, cycling, public transportation, and private car. If applicable, the routes have been calculated for different times of the day (rush hour, midday, off-peak), and assuming different physical abilities (such as walking and cycling speeds), see details below.

The grid follows the geometric properties and enumeration of the versatile Yhdyskuntarakenteen seurantajärjestelmä (YKR) grid used in applications across many domains in Finland, and covers the municipalities of Helsinki, Espoo, Kauniainen, and Vantaa in the Finnish capital region.

Data formats

The data is available in multiple different formats that cater to different requirements, such as different software environments. All data formats share a common set of columns (see below), and can be used interchangeably.

Helsinki_Travel_Time_Matrix_2023.csv.zst: comma-separated values (CSV) of all data columns, without geometries. This data set contains all routes in one file, and can be filtered by origin or destination according to the analysis at hand. The data records can also be joined to the geometries as available below. The file is compressed using the Zstandard algorithm, that many data science libraries, for instance, pandas, support transparently, directly, and automatically.

Helsinki_Travel_Time_Matrix_2023_travel_times.gpkg.zip: an OGC GeoPackage standard file containing all data columns and the geometries that relate to the destination grid cell. The data set is delivered as a ZIP archive, which many GIS systems and libraries, e.g., GDAL/OGR, QGIS, or geopandas, support natively.

Helsinki_Travel_Matrix_2023_travel_times.csv.zip: a set of 13132 comma-separated value files containing the routes to one destination grid cell each. The files contain all data columns, no geometry, and can be joined to the geometries as available below. Filenames of the individual files within the ZIP archive follow the pattern Helsinki_Travel_Time_Matrix_2023_travel_times_to_5787545.csv where 5787545 is replaced by the to_id by which the rows in the file are grouped. Use the from_id column to join with the geometries from one of the files below.

Geometry, only:

Helsinki_Travel_Time_Matrix_2023_grid.gpkg.zip: an OGC GeoPackage standard file containing the geometries and IDs of the grid used in the analysis. This file can be joined both to the from_id and to_id columns of the data files. The data set is delivered as a ZIP archive, which many GIS systems and libraries, e.g., GDAL/OGR, QGIS, or geopandas, support natively.

Helsinki_Travel_Time_Matrix_2023_grid.shp.zip: an ESRI Shapefile archive containing the geometries and IDs of the grid used in the analysis. This file can be joined both to the from_id and to_id columns of the data files.

Table structure

from_id: ID number of the origin grid cell to_id: ID number of the destination grid cell walk_avg: Travel time in minutes from origin to destination by walking at an average speed walk_slo: Travel time in minutes from origin to destination by walking slowly bike_avg: Travel time in minutes from origin to destination by cycling at an average speedbike_fst: Travel time in minutes from origin to destination by cycling fastbike_slo: Travel time in minutes from origin to destination by cycling slowlypt_r_avg: Travel time in minutes from origin to destination by public transportation in rush hour traffic, walking at an average speed pt_r_slo: Travel time in minutes from origin to destination by public transportation in rush hour traffic, walking at a slower speed pt_m_avg: Travel time in minutes from origin to destination by public transportation in midday traffic, walking at an average speed pt_m_slo: Travel time in minutes from origin to destination by public transportation in midday traffic, walking at a slower speed pt_n_avg: Travel time in minutes from origin to destination by public transportation in nighttime traffic, walking at an average speed pt_n_slo: Travel time in minutes from origin to destination by public transportation in nighttime traffic, walking at a lower speed car_r: Travel time in minutes from origin to destination by private car in rush hour traffic car_m: Travel time in minutes from origin to destination by private car in midday traffic car_n: Travel time in minutes from origin to destination by private car in nighttime traffic walk_d: Distance from origin to destination, in meters, on foot

Data for 2013, 2015, and 2018

At the Digital Geography Lab, we started computing travel time matrices in 2013. Our methodology has changed in between the iterations, and naturally, there are systematic differences between the iterations' results. Not all input data sets are available to recompute the historical matrices with new methods, however, we were able to repeat the 2018 calculation using the same methods as the 2023 data set, please find the results below, in the same format.

For the travel time matrices for 2013 and 2015, as well as for 2018 using an older methodology, please refer to DOI:10.5281/zenodo.3247563.

Methodology

Computations were carried out for Wednesday, 15 February, 2023, and Monday, 29 January, 2018, respectively. 'Rush hour' refers to an 1-hour window between 8 and 9 am, 'midday' to 12 noon to 1 pm, and 'nighttime' to 2-3 am.

All routes have been calculated using r5py, a Python library making use of the R5 engine by Conveyal, with modifications to consider local characteristics of the Helsinki use case and to inform the computation models from local real-world data sets. In particular, we made the following modifications:

Walking

Walking speeds, and in turn walking times, are based on the findings of Willberg et al., 2023, in which we measured walking speeds of people of different age groups in varying road surface conditions in Helsinki. Specifically, we chose to use the average measured walking speed in summer conditions for walk_avg (as well as the respective pt_*_walk_avg), and the slowest quintile of all measured walker across all conditions for walk_slo (and the respective pt_*_walk_slo).

Cycling

Cycling speeds are derived from two input data sets. First, we averaged cycling speeds per network segment from Strava data, and computed a ratio between the speed ridden in each segment and the overall average speed. We then use these ratios to compute fast, slow, and average cycling speeds for each segment, based on the mean overall Strava speed, the mean speeds cycled in the Helsinki City Bike bike-share system, and the mean between the two.

Further, in line with the values observed by Jäppinen (2012), we add a flat 30 seconds each for unlocking and locking the bicycle at the origin and destination.

Public Transport

We used public transport schedules in General Transit Feed Specification (GTFS) format published by the Helsinki Regional Transport Authority, and adjusted the walking speeds (for connections between vehicles, as well as for access and egress to and from public transport stops) using the same methods as described above for walking.

Private motorcar

To represent road speeds actually driven in the Helsinki metropolitan region, we used floating car data of a representative sample of the roads in the region to derive the differences between the speed limit and the driven speed on different road classes, and by speed limit, see Perola (2023) for a detailed description of the methodology. Because these per-segment speeds factor in potential waiting times at road crossings, we eliminated turn penalties from R5.

Our modifications were carried out in two ways: some changes can be controlled by preparing input data sets in a certain way, or by setting model parameters outside of R5 or r5py. Other modifications required more profound changes to the source code of the R5 engine.

You can find a fully patched fork of the R5 engine in the Digital Geography Lab's GitHub repositories at github.com/DigitalGeographyLab/r5. The code that handles input data mangling and model parameter estimations is kept together with the logic to read input parameters and to collate output data, in the repository at github.com/DigitalGeographyLab/Helsinki-Travel-Time-Matrices.

The friction (cost allocation/effort) surface was assembled using three primary input datasets on land surface characteristics that help or hinder travel speeds: land cover, roads and topography. Landcover data were from the ESA CCI Landcover map for Africa 2016, roads data were merged from Open-Street Map (OSM) and the MapwithAi project and topography was taken from the SRTM Digital Elevation Model. The costs for travel consider walking/pedestrian travel in this data, but the software is supplied with an easy to change set of travel speeds so they can be adapted easily to consider travel speeds reflecting motorised transportation use. We have reduced the walking speeds to reflect the fact that adults walking with children move approximately 22% slower. There are two friction surfaces provided, the first defines open water as a barrier to travel and so the speed allocated to this landcover is NA. The second defines open water with an associated speed (1 km/hr). To create a walking speed array, first the road walking speeds were used and then missing values were filled with landcover walking speed values. This walking speed array was multiplied by the slope impact grid. The speed for each cell was converted from kilometers per hour to meters per second. Finally, the time (in seconds) to walk across each cell was calculated. The outputs are 20-m spatial resolution geotiffs indicating the time to walk across each cell. They are subsequently used in the least cost path analysis to estimate travel time to the nearest health facilities. However,these friction surfaces can be used by others to estimate travel speed to other destinations in a GIS.

Data used to model Colonel William Leake's journey in the southern Mani Peninsula, Greece, in the year 1805. Leake's journey is described in the book, Travels in the Morea: Volume I (Leake 1830, pp. 233-321). The data may be used to calculate least-cost paths between the places where Leake stopped, taking into consideration the contemporary path network and calculating cost in time based on Tobler's hiking function and the Modified Tobler function. A paper interpreting these data, 'Reconstructing Historical Journeys with Least-Cost Analysis: Colonel William Leake in the Mani Peninsula, Greece,' is published in Journal of Archaeological Science: Reports and can be accessed here: https://doi.org/10.1016/j.jasrep.2019.01.014. The article pre-print can be accessed here: https://works.bepress.com/rebecca-seifried/11/. Dr. Rebecca M. Seifried mapped the pre-modern paths as part of a PhD dissertation completed in 2016 through the Department of Anthropology at the University of Illinois at Chicago, entitled 'Community Organization and Imperial Expansion in a Rural Landscape: The Mani Peninsula, Greece (AD 1000-1821)' (https://hdl.handle.net/10027/21274). Fieldwork was conducted in 2014 and 2016 under the auspices of the 5th Ephorate of Byzantine Antiquities in Sparta and in collaboration with the Diros Project, an archaeological survey and excavation co-directed by Dr. Giorgos Papathanassopoulos and Dr. Anastasia Papathanasiou through the Ephorate of Palaeoanthropology & Speleology of Southern Greece. The remaining datasets were created in collaboration with Dr. Chelsea A.M. Gardner as part of the 'CART-ography Project: Cataloguing Ancient Routes and Travels in the Mani Peninsula,' whose goal is to catalogue the historic accounts of travelers to Mani and to model their routes throughout the peninsula. This research was funded by the National Science Foundation (BCS-1346694), Marie Sklodowska-Curie Actions (H2020-MSCA-IF-2016 750843), the DigitalGlobe Foundation, the National Cadastre and Mapping Agency, SA (Ktimatologio), ArchaeoLandscapes Europe, the University of Illinois at Chicago, the Society of Women Geographers, the Archaeological Institute of America, and Mount Allison University.

Data used to model Colonel William Leake's journey in the southern Mani Peninsula, Greece, in the year 1805. Leake's journey is described in the book, 'Travels in the Morea: Volume I' (Leake 1830, pp. 233-321). The data may be used to calculate least-cost paths between the places where Leake stopped, taking into consideration the contemporary path network and calculating cost in time based on Tobler's hiking function and the Modified Tobler function. A paper interpreting these data, ‘Reconstructing Historical Journeys with Least-Cost Analysis: Colonel William Leake in the Mani Peninsula, Greece,’ is currently under review and will be linked here after acceptance.

Dr. Rebecca M. Seifried mapped the pre-modern paths as part of a PhD dissertation completed in 2016 through the Department of Anthropology at the University of Illinois at Chicago, entitled ‘Community Organization and Imperial Expansion in a Rural Landscape: The Mani Peninsula, Greece (AD 1000-1821)’ (https://indigo.uic.edu/handle/10027/21274). Fieldwork was conducted in 2014 and 2016 under the auspices of the 5th Ephorate of Byzantine Antiquities in Sparta and in collaboration with the Diros Project, an archaeological survey and excavation co-directed by Dr. Giorgos Papathanassopoulos and Dr. Anastasia Papathanasiou through the Ephorate of Palaeoanthropology & Speleology of Southern Greece. The remaining datasets were created in collaboration with Dr. Chelsea A.M. Gardner as part of the "CART-ography Project: Cataloguing Ancient Routes and Travels in the Mani Peninsula,” whose goal is to catalogue the historic accounts of travelers to Mani and to model their routes throughout the peninsula.

This research was funded by the National Science Foundation (BCS-1346694), Marie Sklodowska-Curie Actions (H2020-MSCA-IF-2016 750843), the DigitalGlobe Foundation, the National Cadastre and Mapping Agency, SA (Ktimatologio), ArchaeoLandscapes Europe, the University of Illinois at Chicago, the Society of Women Geographers, the Archaeological Institute of America, and Mount Allison University.

The friction (cost allocation/effort) surface was assembled using three primary input datasets on land surface characteristics that help or hinder travel speeds: land cover, roads and topography. Landcover data were from the ESA CCI Landcover map for Africa 2016, roads data were merged from Open-Street Map (OSM) and the MapwithAi project and topography was taken from the SRTM Digital Elevation Model. The costs for travel consider walking/pedestrian travel in this data, but the software is supplied with an easy to change set of travel speeds so they can be adapted easily to consider travel speeds reflecting motorised transportation use. We have reduced the walking speeds to reflect the fact that adults walking with children move approximately 22% slower. There are two friction surfaces provided, the first defines open water as a barrier to travel and so the speed allocated to this landcover is NA. The second defines open water with an associated speed (1 km/hr). To create a walking speed array, first the road walking speeds were used and then missing values were filled with landcover walking speed values. This walking speed array was multiplied by the slope impact grid. The speed for each cell was converted from kilometers per hour to meters per second. Finally, the time (in seconds) to walk across each cell was calculated. The outputs are 20-m spatial resolution geotiffs indicating the time to walk across each cell. They are subsequently used in the least cost path analysis to estimate travel time to the nearest health facilities. However,these friction surfaces can be used by others to estimate travel speed to other destinations in a GIS.

The coastal recreation study is designed to establish a baseline characterization of participation rates and the economic value of coastal recreation and provide a spatial baseline of coastal recreation use patterns in the North Central Coast region. Please see the project report for full details on the data collection and analysis methods as well as a listing of survey questions. Included in this data package is a summary of all survey data as well as a PDF map depicting intensity of use across all coastal recreation activities data collected in the region. We also have spatial data for individual activities, including: Scenic enjoyment; Beach going (dog-walking, kite-flying, jogging, etc.); Photography; Watching birds and/or other marine life from shore; Sitting in your car watching the scene; Biking or hiking; Collection of non-living resources/beachcombing (agates, fossils, driftwood); and Swimming or body surfing in the ocean. If you would like to access the GIS data from this project please contact us. This dataset was originally uploaded to Oceanspaces (http://oceanspaces.org/) in 2013 as part of the North Central Coast baseline monitoring program. In 2020 the baseline data and reports were uploaded to the California Ocean Protection Council Data Repository by Mike Esgro (Michael.Esgro@resources.ca.gov) and Rani Gaddam (gaddam@ucsc.edu). Every attempt has been made to include all of the original data, metadata, and reports submitted in 2013, but please contact the Data Set Contacts with any questions. The long-term California MPA boundary and project info tables referenced in this dataset can be found as a separate dataset here: https://opc.dataone.org/view/doi:10.25494/P64S3W

This information is being published so that can be used for research projects / ransparency, and recognised that the data is both detailed, and requires specialist GIS software for analysis Between 23 November 2023 and 4 February 2024, we held a significant public consultation looking at 10 policies to deliver a more sustainable future for York’s transport. There are ten policy areas which we believe will help us deliver our ambitions for transport: 1. Accessibility - so that everyone can access the areas and facilities they need and want 2. Improving walking, wheeling and cycling - so that these become real alternatives to driving a car 3. Shaping healthy places - offering a range of ways to move around and using the opportunity to provide better places for us to live, work and visit 4. Improving public transport – upgrading and improving our bus and rail services 5. Safeguarding the environment by cutting carbon, air pollution and noise - meeting climate change targets and improving the health of the city 6. Create a Movement and Place plan - creating safe, connected transport networks for residents, businesses and visitors 7. Reduce car dependency- supporting people to change how they travel, and encourage those who can, to reduce their journeys by car 8. Improving freight and logistics - creating efficient access for businesses while reducing the impact of heavy vehicles 9. Effective maintenance and enforcement - so that people choosing sustainable travel are safe, and that cycling, walking and wheeling routes are well maintained 10. Monitoring the transport network and financing the changes - to ensure the effectiveness of our policies and attract funding to deliver York's new transport strategy as effectively as possible This was to help prepare a new Local Transport Plan by summer 2024. This consultation will help us write a plan and pave the way to work with the new mayor of the incoming York and North Yorkshire Combined Authority. Once our plan is complete, we will then put our case to government and seek funding for the improvements we want to make in the decades to come. The consultation closed on 4 February 2024. For more information on this consultation please click on the link below: https://www.york.gov.uk/BigTransportConversation

Handheld GPS Device Market Outlook

The global handheld GPS device market size was valued at approximately USD 3.1 billion in 2023 and is anticipated to reach USD 5.4 billion by 2032, growing at a CAGR of 6.2% during the forecast period. This robust growth is driven by several factors, including technological advancements, increasing consumer demand for navigation and tracking systems, and expanding applications in various sectors such as outdoor activities, military operations, and commercial use.

One of the key growth factors is the rapid advancement in GPS technology, which has made handheld devices more accurate, reliable, and user-friendly. Improvements in satellite navigation systems and the integration of GPS with other technologies such as GIS (Geographic Information Systems) have significantly enhanced the functionality of these devices. Moreover, innovations in hardware, including the development of more durable and weather-resistant models, are attracting a growing number of users who require reliable navigation tools in harsh environmental conditions.

Another critical driver is the growing popularity of outdoor recreational activities such as hiking, trekking, and geocaching. Enthusiasts of these activities require precise and dependable navigation tools, leading to increased demand for handheld GPS devices. Additionally, as more people participate in outdoor sports and adventures, the market for handheld GPS devices is expected to expand further. Furthermore, the prevalence of e-commerce platforms has made it easier for consumers to access a wide variety of GPS devices, thus boosting market growth.

The military and defense sector also significantly contributes to the growth of the handheld GPS device market. With the increasing need for real-time navigation, tracking, and targeting, these devices have become indispensable tools for modern military operations. The rising defense budgets of various countries and the continuous modernization of military equipment are expected to support steady demand in this segment. Moreover, government initiatives to enhance security and surveillance capabilities are further fueling the adoption of advanced GPS devices.

The rise in Outdoor Sports GPS Products Sales is a testament to the increasing interest in outdoor activities and adventures. As more individuals engage in sports such as hiking, mountain biking, and trail running, the demand for reliable GPS devices tailored to these activities has surged. These products offer enhanced features like topographic mapping, route planning, and real-time tracking, which are essential for safety and navigation in remote areas. The convenience of having a dedicated device that provides accurate location data without relying on cellular networks is a significant advantage for outdoor enthusiasts. This trend is further supported by the growing availability of GPS devices in various price ranges, making them accessible to a broader audience. The integration of advanced technologies such as Bluetooth connectivity and smartphone compatibility also enhances the user experience, allowing seamless data sharing and analysis.

Regionally, North America holds a dominant position in the handheld GPS device market, followed by Europe and Asia Pacific. The strong presence of leading market players, high adoption rates of advanced technologies, and a robust infrastructure for outdoor activities contribute to North America's leadership. Europe benefits from a well-established outdoor recreation sector, while Asia Pacific is witnessing rapid growth due to increasing disposable incomes and a growing interest in outdoor and adventure sports. These regional dynamics are expected to shape the market trends and opportunities in the coming years.

Product Type Analysis

The handheld GPS device market can be segmented by product type into outdoor handheld GPS, marine handheld GPS, aviation handheld GPS, and others. Each of these segments addresses specific user needs and applications, catering to different market demands. Outdoor handheld GPS devices are particularly popular among hikers, trekkers, and adventure enthusiasts. These devices are designed to be rugged, water-resistant, and equipped with features that ensure accurate navigation and tracking in diverse terrains. The increasing interest in outdoor recreational activities is expected to drive the growth of this segment significantly.

Marine handheld GPS devices

The Montgomery Parks trails are used for active and passive recreation and exploring. Much of our trail system is also used for commuting and connecting to the greater street network and trail network of the County. With over 200 miles of natural surface trail, over 70 miles of paved, and paths in nearly every one of our 421 parks, its necessary that each trail segment is recorded as it changes park unit, user type, owner type, surface type, and other aspects.Natural Surface Trails – Natural surface trails are typically narrow (2-4 feet wide) dirt trails. Types of uses associated with these trails are hiking, horseback riding, and all-terrain biking. Unless noted otherwise on the trail map, natural surface trails are “shared by all”.Hard Surface Trails – Hard surface trails may include asphalt paths but they may also be any firm and stable surface capable of supporting casual walkers and cyclists.Park Trails in Montgomery County, MD with emphasis on the M-NCPPC Montgomery Parks network. There is also information on trails provided by other entities. Only officially sanctioned trails are displayed on public interface, however internal users can view trail units in their varying stages of construction or unsanctioned status.The data is used for analysis as it pertains to trail and amenity planning, regional network planning, route and program planning. The data is also used on our public facing materials such as maps and webpages.Contact the Parks GIS Team for more information via email: MCParksGIS@montgomeryparks.org.Data LinksAGOL Feature Service: https://mcplanning.maps.arcgis.com/home/item.html?id=4a5674832aea4ffd9b86812531b12170Services:https://montgomeryplans.org/server/rest/services/Parks/ParkUnits_Py/MapServerhttps://montgomeryplans.org/server/rest/services/Parks/ParkUnits_Py/FeatureServer

This data contains general information about Pedestrian Network in Hong Kong. Pedestrian Network is a set of 3D line features derived from road features and road furniture from Lands Department and Transport Department. A number of attributes are associated with the pedestrian network such as spatially related street names. Besides, the pedestrian network includes information like wheelchair accessibility and obstacles to facilitate the digital inclusion for the needy. Please refer to this video to learn how to use 3D Pedestrian Network Dataset in ArcGIS Pro to facilitate your transportation analysis.The data was provided in the formats of JSON, GML and GDB by Lands Department and downloaded via GEODATA.GOV.HK website.

The original data files were processed and converted into an Esri file geodatabase. Wheelchair accessibility, escalator/lift, staircase walking speed and street gradient were used to create and build a network dataset in order to demonstrate basic functions for pedestrian network and routing analysis in ArcMap and ArcGIS Pro. There are other tables and feature classes in the file geodatabase but they are not included in the network dataset, users have to consider the use of information based on their requirements and make necessary configurations. The coordinate system of this dataset is Hong Kong 1980 Grid.

The objectives of uploading the network dataset to ArcGIS Online platform are to facilitate our Hong Kong ArcGIS users to utilize the data in a spatial ready format and save their data conversion effort.

For details about the schema and information about the content and relationship of the data, please refer to the data dictionary provided by Lands Department at https://geodata.gov.hk/gs/download-datadict/201eaaee-47d6-42d0-ac81-19a430f63952.

For details about the data, source format and terms of conditions of usage, please refer to the website of GEODATA STORE at https://geodata.gov.hk.Dataset last updated on: 2022 Oct

Dundee Waterfront – Walking and Cycling Counts This data set is sourced from Dundee City Council’s Public Space Camera Surveillance System. It shows a count of people walking and cycling on Marine Parade Walk (Camera 332). The data set shows a count of people walking and cycling within these areas every Monday, Wednesday and Saturday during the period 1pm-2pm.This data is experimental and subject to further refinement. Please note that due the nature of CCTV cameras at times data may not be collected as specified above. Therefore, caution should be exercised when analysing data and drawing conclusions for this data set.CCTV datasets contain information on object detections taken from a selection of the CCTV cameras throughout Dundee City. CCTV images are translated into object counts, objects counted include ‘person’, ‘car’, ‘bicycle’, ‘bus’, ‘motorcycle', 'truck, ‘pickup truck 'and ‘van’. The data is generated and owned by Dundee City Council. Copyright © Dundee City Council 2022. This dataset is available for use under the Open Government Licence.Background information about the Dundee CCTV cameras including a map showing the location of the cameras is available on the Dundee City Council website and can be accessed using the following link:https://www.dundeecity.gov.uk/service-area/city-development/sustainable-transport-and-roads/dundees-public-space-camera-surveillance-system