This dataset includes the listing prices for the sale of properties (mostly houses) in Ontario. They are obtained for a short period of time in July 2016 and include the following fields: - Price in dollars - Address of the property - Latitude and Longitude of the address obtained by using Google Geocoding service - Area Name of the property obtained by using Google Geocoding service

This dataset will provide a good starting point for analyzing the inflated housing market in Canada although it does not include time related information. Initially, it is intended to draw an enhanced interactive heatmap of the house prices for different neighborhoods (areas)

However, if there is enough interest, there will be more information added as newer versions to this dataset. Some of those information will include more details on the property as well as time related information on the price (changes).

This is a somehow related articles about the real estate prices in Ontario: http://www.canadianbusiness.com/blogs-and-comment/check-out-this-heat-map-of-toronto-real-estate-prices/

I am also inspired by this dataset which was provided for King County https://www.kaggle.com/harlfoxem/housesalesprediction

The Scotland Heat Map provides estimates of annual heat demand for almost 3 million properties in Scotland. Demand is given in kilowatt-hours per year (kWh/yr). Property level estimates can be combined to give values for various geographies. Both domestic and non-domestic properties are included. This raster dataset gives the total estimated heat demand of properties within 250m x 250m grid squares covering all of Scotland. Heat demand is calculated by combining data from a number of sources, ensuring that the most appropriate data available is used for each property. The data can be used by local authorities and others to identify or inform opportunities for low carbon heat projects such as district heat networks. The Scotland Heat Map is produced by the Scottish Government. The most recent version is the Scotland Heat Map 2022, which was released to local authorities in November 2023. More information can be found in the documentation available on the Scottish Government website: https://www.gov.scot/publications/scotland-heat-map-documents/

This map is intended for use with the KCHA Sites with Urban Heat Mapping and LCI Opportunity Areas: Map Viewer application.King County Housing Authority (KCHA) properties were geocoded with the KC Geocoder, then overlaid with Urban Heat Mapping (Afternoon) data to extract temperature values. Properties were also overlaid with Land Conservation Initiative (LCI) Opportunity Areas (2020 version) to tag them as being in or out of the primary qualifying criteria.More information on the heat mapping project is available in Heat Watch Report for Seattle & King County (PDF file). Contact CAPA Strategies for questions on the data, maps, and data analysis methods.More information on the Land Conservation Initiative Opportunity Areas data can be found here.

The Scotland Heat Map provides estimates of heat demand for all properties in Scotland. To indicate reliability, each estimate is assigned a confidence level from 1 to 5. Level 1 is least reliable and level 5 most. This is mainly determined by the presence of information that would directly impact on heat demand in the estimate's source data. For example, estimates based on data that includes building type, age and floor area would be more reliable than estimates based solely on floor area derived from mapping data. This raster dataset gives the average (mean) confidence level of properties within 1km x 1km grid squares covering all of Scotland. The Scotland Heat Map is a tool to help plan for the reduction of carbon emissions from heat in buildings. Average confidence level is an indicator of reliability of the heat demand estimates within an area and allows planners to decide whether they meet their needs. The map is produced by the Scottish Government. The most recent version is the Scotland Heat Map 2022, which was released to local authorities in November 2023. More information can be found in the documentation available on the Scottish Government website: https://www.gov.scot/publications/scotland-heat-map-documents/

This is an all-purpose viewer application for the Cleveland property survey 2022 results. It offers a lookup tool, various heat maps, and reporting by criteria that the user can choose.InstructionsViewer pageThe main view for looking up and searching property surveys. The heatmap is fixed to show clusters of D and F properties to guide the user's eyes to areas to explore further.Heatmaps pageExplore different clusters of the grades in this view. Switching back to Viewer will pan the map to the same place.Charts pageSee summary statistics about a given selection of property surveys, starting by default with all surveys. Use filters on the left to narrow down your interest and understand relationships between variables.Data GlossaryFor more information about the dataset, see the City-version of 2022 WRLC Property Survey layerThis app uses the following dataset(s):Citywide Property Survey 2022ContactsDro Sohrabian, Urban Analytics & Innovation

Georeferenced Amsterdam Rental Values

Exploring Urban Housing Markets Through Historical Patterns

By [source]

About this dataset

This dataset provides insightful and comprehensive information on the spatial distribution of rental values in Amsterdam throughout a period of time. In order to generate this data, the Verponding registration from Amsterdam City Archives was consulted, which collected a tax known as the Verpondings-quohieren van den 8sten penning on the rental value of immovable property. This data was attained through transcribing and geo-referencing registration books from the archives; thereby incorporating both transcribed rental values of all real estate properties listed therein as well as geo-referenced tax records plotted onto an historical map of Amsterdam.

The compilation and analysis of historic rental values may offer further insights into underlying social, economic, and cultural developments within Amsterdam over time. Therefore, the potential applications for this dataset are enormous; offering investigators an opportunity to gather useful information with relation to urban renewal efforts or even supporting archaeological research studies. Moreover, with various columns such as order number, wijk district where applicable property is located within respective street name as well as details on whether said property is available for rent/own disposition - researchers may also utilize these collected metrics for meaningful planning/management decisions simultaneously unfolding hidden patterns concerning disparities or trends that might be discerned when compared to current trends employed by residents today

More Datasets

For more datasets, click here.

Featured Notebooks

• 🚨 Your notebook can be here! 🚨!

How to use the dataset

This dataset provides insight into the spatial distribution of rental values in Amsterdam between 1647 and 1652. The data provided is a valuable resource for researchers looking to study the economic, social, and cultural history of Amsterdam over this period in time. With this data set, users can explore hidden patterns, disparities, and trends that may inform decision-making or help with urban renewal projects. Moreover, this dataset can also be used to assess archaeological and cultural heritage research.

In order to understand the georeferenced rental values better and draw meaningful conclusions from the data set it is important to keep few things in mind: - Check out handy columns such as ‘wijk’ (district) which offers information about where each property is located;
- The ‘rent/own’ indicates whether a property was rented (huur) or owned (koop);
- The ‘value’ column contains information regarding the rental value of each property; - The ‘tax’ column shows how much tax was paid on each listed property;
- In addition to this additional notes have been provided in some cases offering more insights into particular properties;

By seeing all these details together one will get an excellent overview of individual households renting or owning their real estate properties along with their tax payment throughout Amsterdam during this period 1647-1652. Additionally by graphing this data one could compare rental value against geographic location or even track consecutive years on how they vary year after year! This can help trace any historical changes taking place how they affect individual households within Amsterdam as well as socio-economic changes occurring throughout the city over the years!

Research Ideas

• Creating a predictive heat map by analyzing correlation between rental values and various other factors such as geographic location, proximity to public transportation, availability of amenities/services etc.
• Comparing and contrasting current maps of real estate prices in Amsterdam with the maps from this dataset to analyze shifts in property prices over time and understand the evolution of urban housing markets in the city.
• Studying socio-economic differences between different geographical areas based on rental values from this dataset, which could help provide a better understanding of the social, economic, and cultural history of the city

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

License: CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication No Copyright - You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permi...

Heat Map/Points showing new Single Family Housing for Years 2012-2016 including: Parcel Number, Category, Issued Date, Valuation, Square Footage, and Year.By using this dataset you acknowledge the following:Kansas Open Records Act StatementThe Kansas Open Records Act provides in K.S.A. 45-230 that "no person shall knowingly sell, give or receive, for the purpose of selling or offering for sale, any property or service to persons listed therein, any list of names and addresses contained in, or derived from public records..." Violation of this law may subject the violator to a civil penalty of $500.00 for each violation. Violators will be reported for prosecution.By accessing this site, the user makes the following certification pursuant to K.S.A. 45-220(c)(2): "The requester does not intend to, and will not: (A) Use any list of names or addresses contained in or derived from the records or information for the purpose of selling or offering for sale any property or service to any person listed or to any person who resides at any address listed; or (B) sell, give or otherwise make available to any person any list of names or addresses contained in or derived from the records or information for the purpose of allowing that person to sell or offer for sale any property or service to any person listed or to any person who resides at any address listed."

Notice: this is not the latest Heat Island Severity image service. For 2023 data, visit https://tpl.maps.arcgis.com/home/item.html?id=db5bdb0f0c8c4b85b8270ec67448a0b6. This layer contains the relative heat severity for every pixel for every city in the United States. This 30-meter raster was derived from Landsat 8 imagery band 10 (ground-level thermal sensor) from the summers of 2018 and 2019.Federal statistics over a 30-year period show extreme heat is the leading cause of weather-related deaths in the United States. Extreme heat exacerbated by urban heat islands can lead to increased respiratory difficulties, heat exhaustion, and heat stroke. These heat impacts significantly affect the most vulnerable—children, the elderly, and those with preexisting conditions.The purpose of this layer is to show where certain areas of cities are hotter than the average temperature for that same city as a whole. Severity is measured on a scale of 1 to 5, with 1 being a relatively mild heat area (slightly above the mean for the city), and 5 being a severe heat area (significantly above the mean for the city). The absolute heat above mean values are classified into these 5 classes using the Jenks Natural Breaks classification method, which seeks to reduce the variance within classes and maximize the variance between classes. Knowing where areas of high heat are located can help a city government plan for mitigation strategies.This dataset represents a snapshot in time. It will be updated yearly, but is static between updates. It does not take into account changes in heat during a single day, for example, from building shadows moving. The thermal readings detected by the Landsat 8 sensor are surface-level, whether that surface is the ground or the top of a building. Although there is strong correlation between surface temperature and air temperature, they are not the same. We believe that this is useful at the national level, and for cities that don’t have the ability to conduct their own hyper local temperature survey. Where local data is available, it may be more accurate than this dataset. Dataset SummaryThis dataset was developed using proprietary Python code developed at The Trust for Public Land, running on the Descartes Labs platform through the Descartes Labs API for Python. The Descartes Labs platform allows for extremely fast retrieval and processing of imagery, which makes it possible to produce heat island data for all cities in the United States in a relatively short amount of time.What can you do with this layer?This layer has query, identify, and export image services available. Since it is served as an image service, it is not necessary to download the data; the service itself is data that can be used directly in any Esri geoprocessing tool that accepts raster data as input.Using the Urban Heat Island (UHI) Image ServicesThe data is made available as an image service. There is a processing template applied that supplies the yellow-to-red or blue-to-red color ramp, but once this processing template is removed (you can do this in ArcGIS Pro or ArcGIS Desktop, or in QGIS), the actual data values come through the service and can be used directly in a geoprocessing tool (for example, to extract an area of interest). Following are instructions for doing this in Pro.In ArcGIS Pro, in a Map view, in the Catalog window, click on Portal. In the Portal window, click on the far-right icon representing Living Atlas. Search on the acronyms “tpl” and “uhi”. The results returned will be the UHI image services. Right click on a result and select “Add to current map” from the context menu. When the image service is added to the map, right-click on it in the map view, and select Properties. In the Properties window, select Processing Templates. On the drop-down menu at the top of the window, the default Processing Template is either a yellow-to-red ramp or a blue-to-red ramp. Click the drop-down, and select “None”, then “OK”. Now you will have the actual pixel values displayed in the map, and available to any geoprocessing tool that takes a raster as input. Below is a screenshot of ArcGIS Pro with a UHI image service loaded, color ramp removed, and symbology changed back to a yellow-to-red ramp (a classified renderer can also be used): Other Sources of Heat Island InformationPlease see these websites for valuable information on heat islands and to learn about exciting new heat island research being led by scientists across the country:EPA’s Heat Island Resource CenterDr. Ladd Keith, University of Arizona Dr. Ben McMahan, University of Arizona Dr. Jeremy Hoffman, Science Museum of Virginia Dr. Hunter Jones, NOAADaphne Lundi, Senior Policy Advisor, NYC Mayor's Office of Recovery and ResiliencyDisclaimer/FeedbackWith nearly 14,000 cities represented, checking each city's heat island raster for quality assurance would be prohibitively time-consuming, so The Trust for Public Land checked a statistically significant sample size for data quality. The sample passed all quality checks, with about 98.5% of the output cities error-free, but there could be instances where the user finds errors in the data. These errors will most likely take the form of a line of discontinuity where there is no city boundary; this type of error is caused by large temperature differences in two adjacent Landsat scenes, so the discontinuity occurs along scene boundaries (see figure below). The Trust for Public Land would appreciate feedback on these errors so that version 2 of the national UHI dataset can be improved. Contact Dale.Watt@tpl.org with feedback.

The Scotland Heat Map provides the locations of existing and planned heat networks. Both communal and district heat networks are included. Data about each network includes, where available, heat capacity size category, network name, status (either ‘operational’ or ‘in development’) and the main technology used (for example, ‘boiler’). There is only one point location for each network, the data does not show all connected properties or pipe layouts. Networks can serve domestic properties, non-domestic properties or a mixture of the two.

Heat networks have the potential to reduce carbon emissions from heating buildings. Alongside other heat map datasets, information on existing and planned networks is used to identify further opportunities to reduce carbon emissions. For example, by connecting more buildings to an existing network or by replacing the energy source with a nearby lower carbon alternative.

Data on heat networks comes from two sources. These are: the UK Department for Energy Security and Net Zero’s Heat Networks (Metering and Billing) Regulations (HNMBR) dataset and Zero Waste Scotland’s Low Carbon Heat Database (LCHD). The most recent data available is up to end July 2022 for the HNMBR dataset (though the majority of the HNMBR data included in the heat map is up to end December 2018) and January 2022 for the LCHD. More information can be found in the documentation available on the Scottish Government website: https://www.gov.scot/publications/scotland-heat-map-documents/

Chicago Heights Municipal Atlas Data Layers - March 25, 2020 uploadData LayersWards (2018)* Alderman's name and contact emailCity-Owned Parcels* Unknown year of data, but differs from 2016 Cook County Assessor dataHeritage Preservation Overlay DistrictVoting Precincts (2018)Municipal Boundary (2016)* Differs from Cook County municipal boundary data, but this boundary will be prioritized.Vacant Properties (2016) - Points [for heat map]Vacant Properties (2016) - ParcelsLand Bank Properties (2017)Lawn Maintenance Parcels (2019)

Šotimaa soojuskaart (Scottish Heat Map) annab hinnangud ligi 3 miljoni Šotimaal asuva kinnistu aastase küttevajaduse kohta. Nõudlus esitatakse kilovatt-tundides aasta kohta (kWh/aasta). Omanditaseme hinnanguid saab kombineerida, et saada erinevate geograafiliste piirkondade väärtused. Hõlmatud on nii kodu- kui ka välismaised kinnisvaraobjektid. See andmekogum annab ülevaate kinnisvara hinnangulisest küttevajadusest igas Šotimaa kohaliku omavalitsuse piirkonnas. Soojusnõudluse arvutamiseks ühendatakse mitmest allikast pärit andmed, tagades, et iga kinnisvaraobjekti kohta kasutatakse kõige asjakohasemaid kättesaadavaid andmeid. Kohalikud omavalitsused ja teised saavad neid andmeid kasutada, et teha kindlaks vähese CO2 heitega soojusprojektide, näiteks kaugküttevõrkude võimalused või anda nende kohta teavet. Šotimaa soojuskaarti (Scottish Heat Map) koostab Šotimaa valitsus. Viimane versioon on Scotland Heat Map 2022, mis avaldati kohalikele omavalitsustele 2023. aasta novembris. Lisateavet saab Šotimaa valitsuse veebisaidil kättesaadavatest dokumentidest: https://www.gov.scot/publications/scotland-heat-map-documents/

Land Surface Temperature (LST) is a key indicator of land surface states, and can provide information on surface-atmosphere heat and mass fluxes, vegetation water stress, and soil moisture. A daily, day and night, LST data set for continental Africa, including Madagascar, was derived from Advanced Very High Resolution Radiometer (AVHRR) Global Area Coverage (GAC; 4 km resolution) data for the 6-year lifetime of the NOAA-14 satellite (from 1995 to 2000) using a modified version of the Global Inventory Mapping and Monitoring System (GIMMS) (Tucker et al., 1994). The data were projected into Albers Equal Area and aggregated to 8 km spatial resolution. The data were cloud-filtered with CLAVR-1 algorithm (Stowe et al., 1999). The LST values were estimated with a split-window technique (Ulivieri et al., 1994) that takes advantage of differential absorption of the thermal infrared signal in bands 4 and 5. The emissivity of the surface was generated using a land cover classification map (Hansen et al., 2000) combined with the FAO soil map of Africa (FAO-UNESCO, 1977) and additional maps of tree, herbaceous, and bare soil percent cover (DeFries et al., 2000). Collateral products include cloud mask, time-of-scan, latitude and longitude, and land/water mask files.The data are in flat binary files. Each data file contains 1152 columns and 1152 rows, in signed integer format (2 bytes), with 8 km by 8 km spatial resolution. A unique map exists for each day and each night of the 6-year NOAA-14 lifetime. The data are best used to infer broad temporal and spatial trends rather than pixel-by-pixel values.

The Properties Vulnerable to Heat Impact report, produced by Arup, maps London's heat risk across homes, neighbourhoods, and essential properties in the wake of climate change. The study focused on essential settings, emphasising areas where occupants are especially vulnerable to heat-related hazards. This included schools, hospitals, care homes residential properties and neighbourhoods. Properties Vulnerable to Heat Impact Report | London City Hall

Explore active listings and real-time home values for houses, condominiums, and townhomes in Hot Springs Village AR See prices, sizes, and property types on an interactive map.