Based on the First Revised Series of the Ordnance Survey London Town Plans. You can find a different version of the Georeferenced maps on the National Library of Scotland website:

https://maps.nls.uk/geo/explore/#zoom=11&lat=51.4907&lon=-0.1331&layers=163&b=1

Each factory is coded to indicate whether it is on both or just one of the two 19th century series of Ordnance Survey London Town Plans. I have also tried to catagorize the factories. There are some other incomplete fields or fields used in earlier versions of this database.

Data used in Jim Clifford, West Ham and the River Lea A Social and Environmental History of London’s Industrialized Marshland, 1839–1914, UBC Press, 2017, https://www.ubcpress.ca/west-ham-and-the-river-lea

The GLA have produced a dataset that provides a more accurate estimate of the extent of the London’s green infrastructure - the city’s parks, gardens, trees, green spaces, rivers and wetlands, and features such as green roofs. The green cover layer was created by combining classified near-infrared aerial imagery (NDVI) with land use datasets and resulted in a green cover estimate for London of between 48-51 percent. The baseline is presented as a range to account for variations in the analysis of aerial imagery. The methodology is set out in the report below and a web map created to visualise the data. The final green cover layer is available to download in a geospatial format (shape files). **Contains OS data **© Crown copyright and database rights 2019. Contains Verisk **Analytics ** GeoInformation Group UKMap data. NOTE: The data is based on Ordnance Survey mapping and the data is published under Ordnance Survey's 'presumption to publish'.

This archive contains code and data to go with the paper An Open-Source Automatic Survey of Green Roofs in London using Segmentation of Aerial Imagery.

This archive contains geospatial data, as well as the code used to generate the geospatial data.

The geospatial data consists of georeferenced polygons identifying areas which are covered by green roofs in London (GBR) generated from 2019 aerial imagery.

The data is described in detail in the manuscript An Open-Source Automatic Survey of Green Roofs in London using Segmentation of Aerial Imagery. See abstract below.

GeoJSON format:

GeoJSON is a format for encoding geospatial data, see https://geojson.org/.

GeoJSON can be read using GIS programs including ArcGIS, QGIS, OGR.

Contents:

geospatial_data/buffered_polygons_2021.zip a zip archive containing a geojson file. It is the estimated locations of green roofs in London in 2021 and is the main result, which can be opened in any GIS program after being unzipped.

geospatial_data/buffered_polygons_2019.zip a zip archive containing a geojson file. It is the estimated locations of green roofs in London in 2019 and is a secondary result, which can be opened in any GIS program after being unzipped. The predictions were made with the same model as the 2021 results.

geospatial_data/labelled_area.zip a zip archive containing a geojson file. Identifies the area which was hand-labelled.

geospatial_data/manual_2021.zip a zip archive containing a geojson file. Manually labelled green roof from 2021 imagery.

geospatial_data/manual_2019.zip a zip archive containing a geojson file. Manually labelled green roof from 2019 imagery.

segmentation_code contains the code used to produce the segmentation from the aerial imagery.

analysis_code contains the code used to produce the plots and tables for the paper.

Imagery availability:

Unfortunately the aerial imagery and building footprint data cannot be shared directly, as you will require the proper license. Both can be found at Digimap provided your institution has the license.

Abstract:

Green roofs can mitigate heat, increase biodiversity, and attenuate storm water, giving some of the benefits of natural vegetation in an urban context where ground space is scarce. To guide the design of more sustainable and climate resilient buildings and neighbourhoods, there is a need to assess the existing status of green roof coverage and explore the potential for future implementation. Therefore, accurate information on the prevalence and characteristics of existing green roofs is needed, but this information is currently lacking. Segmentation algorithms have been used widely to identify buildings and land cover in aerial imagery. Using a machine-learning algorithm based on U-Net to segment aerial imagery, we surveyed the area and coverage of green roofs in London, producing a geospatial dataset \cite[]{simpson_charles_2022_6861929}. We estimate that there was 0.23 km^2 of green roof in the Central Activities Zone (CAZ) of London, (1.07 km^2) in Inner London, and (1.89 km^2) in Greater London in the year 2021. This corresponds to 2.0% of the total building footprint area in the CAZ, and 1.3% in Inner London. There is a relatively higher concentration of green roofs in the City of London, covering 3.9% of the total building footprint area. Test set accuracy was 0.99, with an f-score of 0.58. When tested against imagery and labels from a different year (2019), the model performed just as well as a model trained on the imagery and labels from that year, showing that the model generalised well between different imagery. We improve on previous studies by including more negative examples in the training data, and by requiring coincidence between vector building footprints and green roof patches. We experimented with different data augmentation methods, and found a small improvement in performance when applying random elastic deformations, colour shifts, gamma adjustments, and rotations to the imagery. The survey covers 1558 km^2 of Greater London, making this the largest open automatic survey of green roofs in any city. The geospatial dataset is at the single-building level, providing a higher level of detail over the larger area compared to what was already available. This dataset will enable future work exploring the potential of green roofs in London and on urban climate modelling.

Polygon feature class of London Borough Boundaries.Last updated:28/06/17;Mastermap Alignment:N/A

Transport for London's (TFL) Public Transport Accessibility Levels (PTALs)

PTALS are a detailed and accurate measure of the accessibility of a point to the public transport network, taking into account walk access time and service availability. The method is essentially a way of measuring the density of the public transport network at any location within Greater London.

Each ares is graded between 0 and 6b, where a score of 0 is very poor access to public transport, and 6b is excellent access to public transport.

The current methodology was developed in 1992, by the London Borough of Hammersmith and Fulham. The model has been thoroughly reviewed and tested, and has been agreed by the London Borough-led PTAL development group as the most appropriate for use across London.

The measure therefore reflects:

• • Walking time from the point-of interest to the public transport access points;
• • The reliability of the service modes available;
• • The number of services available within the catchment; and
• • The level of service at the public transport access points - i.e. average waiting time.

It does not consider:

• • The speed or utility of accessible services;
• • Crowding, including the ability to board services; or,
• • Ease of interchange.

The PTAL methodology was developed for London where a dense integrated public transport network means that nearly all destinations can be reached within a reasonable amount of time. Research using the ATOS (Access to Opportunities and Services) methodology shows that there is a strong correlation between PTALs and the time taken to reach key services – i.e. high PTAL areas generally have good access to services and low PTAL areas have poor access to services.

Notes

6-digit references identify 100m grid squares.

The 2012 CSV file previously available on the Datastore is now only available via the TfL feeds page.

The 2014 files are available to download below. This includes the GIS contour files.

Current PTAL values can be viewed at TfL’s web site: www.webptals.org.uk

The GLA has calculated the percentage of population for each ward, LSOA, MSOA and borough within each PTAL. The files for 2014 are available below. The method used mapped the number of addresses (using Ordinance Survey AddressBase Plus, and 2011 Census London Output Areas boundaries).

TFL also publish on their website a tool that shows travel time and PTAL maps from any point within London. Click anywhere on the map or input a postcode to change the selected location.

The Zip folder contains a range of key GIS boundary files for ESRI and Map Info covering Greater London.

The folder includes:

- Output Area (OA) 2011,

- Lower Super Output Area (LSOA) 2004 and 2011,

- Middle Super Output Area (MSOA) 2004 and 2011,

- London Wards (two files: City of London merged into single area and split into seperate wards). There are separate download file for 2014 & 2018 boundaries.

- London Boroughs

- Greater London boundary

Note: The OA to MSOA boundaries have been generalised to reduce file size/loading time.

On maps created using these boundaries the copyright must be stated. This is: "Contains National Statistics data © Crown copyright and database right [2015]" and "Contains Ordnance Survey data © Crown copyright and database right [2015]"

For more information about boundary data sharing read these Terms and Conditions of Supply.

The GIS (Geographic Information System) vectorisation project will deliver the incremental digital conversion of our legacy geospatial network records. This dataset defines the sub-areas which will be incrementally delivered, detailing corresponding current status and planned completion dates. This allows users to understand the current and future coverage of digital geospatial network records as the project progresses.

Methodological Approach Progress against a defined project plan is captured and updated throughout the day. A script is run to convert into a shapefile. This shapefile is then uploaded onto the Open Data Portal.

Quality Control Statement

This dataset is provided "as is".

Assurance Statement The Open Data Team has checked outputs to validate.

Other Download dataset information: Metadata (JSON)Definitions of key terms related to this dataset can be found in the Open Data Portal Glossary: https://ukpowernetworks.opendatasoft.com/pages/glossary/

The Zip folder contains a range of key GIS boundary files for ESRI and Map Info covering Greater London. The folder includes: - Output Area (OA) 2011, - Lower Super Output Area (LSOA) 2004 and 2011, - Middle Super Output Area (MSOA) 2004 and 2011, - London Wards (two files: City of London merged into single area and split into seperate wards). There are separate download file for 2014 & 2018 boundaries. - London Boroughs Note: The OA to MSOA boundaries have been generalised to reduce file size/loading time. On maps created using these boundaries the copyright must be stated. This is: "Contains National Statistics data © Crown copyright and database right [2015]" and "Contains Ordnance Survey data © Crown copyright and database right [2015]" For more information about boundary data sharing read these Terms and Conditions of Supply.

These data are all about the station entries/exit totals in different categories for each station in the London Underground system.

There are two types of files:

Entry/Exit Counts:

IMPORTANT:

Entry/Exit counts are daily totals, NOT annual totals. They are counts for one typical day for the whole traffic day. To get annual, apply the following weights:

Annual figures are calculated as entries plus exits, weighting weekdays by 253, Saturdays by 52 and Sundays by 59.

This weighting is based on the assumption that 7 Bank-holidays are represented by 7 Sundays and grossing up to a 364 day year excluding Christmas Day This adjustment does not change from year to year depending on when the Bank Holidays are but is used as an annual factor for comparing one year with another

Total exits for the system should equal total entries but they do not because of inaccuracies in the counts and because counts take place on different days and some counts include NR passengers.

Note Column:

N = station closed T = Figures include all passengers on the W&C line (which is closed on Sundays) B = Figures for Bank/Monument now exclude W&C line passengers interchanging with other lines A = previous year's counts adjusted by average change between years by zone

Columns:

nlc: Station ID

Station Name: Name of station

Borough: Surrounding area name

Note: Any issues with counting during year

Entry_Week: Typical weekday entry totals

Entry_Saturday: Typical Saturday entry totals

Entry_Sunday: Typical Sunday entry totals

Exit_Week: Typical weekday exit totals

Exit_Saturday: Typical Saturday exit totals

Exit_Sunday: Typical Sunday exit totals

AnnualEntryExit_Mill: Entries+Exits for entire year (in millions)

Geospatial Stations:

This is a json file I constructed for a web based visualization project a few years ago. There are three basic keys: 'stations': Station name tied to lat/lon coordinates 'lines': Coordinates of each line on the tube map. Circle, District, etc. 'river': Coordinates of the Thames river.

These are useful for drawing the map as it appears on the classic, colorful map of the London Underground.

The TFL has only updated this data through 2017. When they provide updated figures for the last 4+ years I will update them here.

GIS datasets and CAD files representing assessment parcels, streets, contours, and topographic features. Also includes orthorectified aerial image of entire city.

Available on DVD through the Map and Data Library. DVD #453.

The dataset titled "London ON Zoning By-law No. Z.-1" falls under the domain of Housing and is tagged with keywords such as By-laws, Housing Potential, Land Use, London, and Zoning. It is available in HTML format and covers the geospatial area of London. The dataset is open for access and is owned and published by the City of London. The contact point for any queries regarding access is zoning@london.ca. The dataset was accessed on 3rd March 2025 and has a unique identifier of by-laws/5111. The language of the dataset is English. The dataset does not contain any data about individuals, identifiable individuals, or Indigenous communities. It has a municipal geospatial resolution. The description of the dataset indicates that it contains Zoning By-laws which regulate how land and buildings are used, the location of buildings, lot coverage, building heights, and other provisions necessary for proper development. The resources available in the dataset include 'London ON Zoning By-law No. Z-1' and 'London ON Zoning By-law No. Z-1 - Interactive Map'. The metadata for the dataset was created on 19th March 2025 and was last modified on 26th March 2025.

About Dataset

Dataset Overview This dataset provides a snapshot of real estate transactions in London for 2024. It includes key property details such as the number of bedrooms, bathrooms, living space size, lot size, and transaction price. Additionally, it incorporates information about property features like waterfront views, renovation history, and construction quality. Designed for educational and research purposes, the dataset offers insights into London's real estate market trends and serves as a valuable resource for data analysis and machine learning applications.

Data Science Applications This dataset is ideal for students, researchers, and professionals seeking to apply data science techniques to real-world real estate data. Potential applications include:

Exploratory Data Analysis (EDA): Investigate price trends, property characteristics, and geographical distribution of transactions. Price Prediction Models: Develop machine learning models to predict property prices based on features like size, location, and condition. Trend Analysis: Analyze historical and geographical trends in property prices and features. Geospatial Analysis: Map properties based on latitude and longitude to identify hotspots or underserved areas.

Column Descriptions

Ethically Mined Data This dataset was ethically sourced from publicly available property listings. It does not include any Personally Identifiable Information (PII) or data that could infringe on individual privacy. All information represents public details about properties for sale in London.

Acknowledgements

Data Source: Real estate data provided from publicly accessible resources. Image Credit: Unsplash for real estate-themed visuals. Use this dataset responsibly for educational and analytical purposes!

Download .zipThis file contains point data used for the construction of lake maps for State of Ohio. Data was collected in the Ohio State Plane Coordinate System for both the northern and southern state planes in the Lambert Projection Zone. Except for the lakes in extreme western Ohio which is in UTM zone 16N the majority of lakes are in UTM zone 17N and datum NAD83. The data was collected by fisheries biologists with the Ohio Division of Wildlife using a Trimble GPS Pathfinder Pro XRS receiver and Recon datalogger. Geocoding of depths occurred during water levels that were ± 60 cm of full recreational pool while transversing the reservoir at 100m intervals driving at a vessel speed of 2.0-2.5 m/s. Depth contour lines were derived from this data by creating a raster file from the point bathymetry and boundary lake data. The Spatial Analyst Interpolation tool outputs point data that is then changed into polyline contours using the Spatial Analyst Surface tool. Additional details on the digitizing process are available upon request.Contact Information:GIS Support, ODNR GIS ServicesOhio Department of Natural ResourcesDivision of Wildlife2045 Morse Rd, Bldg G-2Columbus, OH, 43229Telephone: 614-265-6462Email: gis.support@dnr.ohio.gov

Based on the First Series of the Ordnance Survey London Town Plans. Each factory is coded to indicate whether it is on both or just one of the two 19th century series of Ordnance Survey London Town Plans. I have also tried to catagorize the factories. There are some other incomplete fields or fields used in earlier versions of this database. Data used in Jim Clifford, West Ham and the River Lea A Social and Environmental History of London’s Industrialized Marshland, 1839–1914, UBC Press, 2017, https://www.ubcpress.ca/west-ham-and-the-river-lea

GIS files showing the boundary of the Old Oak and Park Royal Development Corporation.

Data is available in either ESRI shapefile or MapInfo TAB format.

https://files.datapress.com/london/wp-uploads/20160728114955/OPDC-boundary-01_0.png" alt="OPDC boundary - 01_0">

This dataset is a general representation of parcel mapping used by the City of London. The mapping is derived from assessment and ownership data but is not sanctioned by either MPAC or Teranet. The geospatial accuracy is not to be relied upon and must not be used for building permit applcaitions, engineering designs, detailed planning, development or property use. Use at own risk.

Download .zipThese files contain the lake boundary data used by the Ohio Division of Wildlife for the construction of lake maps. Lake boundary data was derived by digitizing Ohio Statewide Imagery Program (OSIP-1) data. Additional details on the digitizing process are available on request.

Lake boundary: http://ogrip.oit.ohio.gov/ProjectsInitiatives/StatewideImagery.aspxContact Information:GIS Support, ODNR GIS ServicesOhio Department of Natural ResourcesDivision of Wildlife2045 Morse Rd, Bldg G-2Columbus, OH, 43229Telephone: 614-265-6462Email: gis.support@dnr.ohio.gov

Part of Release: Official Sub-Ward, Ward and Borough level crime counts. This is the most accurate data available for counting numbers of crimes in London according to official recorded crime types, by the month the offence occurred, and by either borough, ward or sub ward location. The page contains the LSOA level file (Pre-2015 data in the 'archive' file) Click here for corresponding ward level data: Recorded Crime Summary Data for London: Ward Level Click here for corresponding borough level data: Recorded Crime Summary Data for London: Borough Level ‘Sub-Ward data’ counts the number of crimes in each sub ward area of London (Census Lower Super Output Area or LSOA) per month according to crime type. Use this data if you need to analyse crime data at a sub ward level. Because not all crimes can be matched to a specific LSOA area, you should not use this data set to count crimes by ward or borough. For these purposes use one of the other datasets according to the level of geographic precision you need. The categories of crime counts within them may change from time to time. Below is a list of the crime types you can currently extract (*only at borough or ward level): Minor Category: Major Category Murder: ViolenceAgainstThePerson CommonAssault: ViolenceAgainstThePerson OffensiveWeapon: ViolenceAgainstThePerson Harassment: ViolenceAgainstThePerson Otherviolence: ViolenceAgainstThePerson AssaultWithInjury: ViolenceAgainstThePerson WoundingGBH: ViolenceAgainstThePerson PersonalProperty: Robbery BusinessProperty: Robbery BurglaryInADwelling: Burglary BurglaryInOtherBuildings: Burglary TheftOrTakingOfMotor: TheftAndHandling TheftFromMotor: TheftAndHandling MotorInterferenceAndTampering: TheftAndHandling TheftFromShops: TheftAndHandling TheftOrTakingOfPedalCycles: TheftAndHandling OtherTheftPerson: TheftAndHandling OtherTheft: TheftAndHandling HandlingStolenGoods: TheftAndHandling CriminalDamageToADwelling: CriminalDamage CriminalDamageToOtherBldg: CriminalDamage CriminalDamageToMotor: CriminalDamage OtherCriminalDamage: CriminalDamage DrugTrafficking: Drugs PossessionOfDrugs: Drugs OtherDrugOffences: Drugs GoingEquipped: OtherNotifiableOffences OtherNotifiable: OtherNotifiableOffences (NB. no Sexual Offences data is included at LSOA level for disclosure purposes) Each row of data in the data sets contains: *The number of incidents according to the Month Recorded, the specific crime type, and the Location *The Month Recorded *The broad crime type (Major HO category – eg Robbery) *The specific crime type (Minor HO category – eg Robbery: Personal Property) *The Location (Sub –Ward, Ward or borough depending on the data set selected)

London View management Framework protected vistas. Zipped file containing the GIS files (Mapinfo and ESRI shape) for the viewing corridors and background assessment areas from Map 7.3 of the London Plan. See more on the GLA website.

Major motorways and trunk roads defined by DfT . Last updated: please refer to the LAST_EDIT_DATE field.