Download: https://s3-ant1.antwerpen.be/prd-open-data-data-tank/raster/Hittekaart_dag.zip - https://s3-ant1.antwerpen.be/prd-open-data-data-tank/raster/Hittekaart_dag.zip Heatmaps SW/EMA The urban heat island effect (SHE) occurs when there is a temperature difference between city and surrounding countryside and is mainly the result of the built-up and paved environment. The SHE is the largest at night and is particularly noticeable during the summer months. The most well-known consequence of this effect is the ‘heat stress’ or the ‘feeling of unease’ in hot temperatures. During heat waves and especially at night, heat stress can lead to health problems and even mortality, especially among sick, young (-4y) and older (+65y) populations. Other consequences of the SHE are the failure of infrastructure, machinery, reduction of labor productivity, reduced service life of materials... The factors behind this radiation temperature map and which thus influence the SHE are: shadow effects, evaporation, wind effects and heat storage and radiation of buildings and materials. So far, there are no concrete objectives or regulations regarding the SHE. However, possible measures that can temper the SHE are: creating maximum shade, minimal paving, natural ventilation corridors, open water features, green design and trees in the public domain, green roofs, green facades, light materials for buildings and the public domain and reducing the ‘sky-view factor’ or ‘building containment’ of the public domain in order to avoid the indirect radiation of materials. The city of Antwerp initially mapped the SHE to develop a prevention and communication policy for its residents, companies and visitors and to implement policy measures to increase the quality of life in the city. In the second instance, this research is part of the preparation of the Antwerp Adaptation Strategy, which systematically maps out the city's challenges with regard to climate change. The same study found that the SHE will increase as a result of global warming.

Tools for building and distributing geographical areas — consisting of five-digit postcodes Areas of responsibility for service stores and sellers Production of freight and distribution zone Create or import points of interest Geographic review of customers, stores, warehouses, staff and much more “Heat Maps” for visualisation of sales, customer concentration, etc. Always current address and postal code data Web service and API

Under the Energy Efficiency Directive and the Renewable Energy Directive, EU Member States were required to report national figures and plans on heat and cold by the end of June 2024. This is in line with the European Energy Union's strategy to achieve carbon neutrality by 2050. The main products are maps for the territory of Flanders with the heat demand at the level of the municipalities and the statistical sectors, maps of the existing and planned heat networks and finally also locations of potential heat supply points. The study was carried out by the Flemish Energy and Climate Agency. You can consult the accompanying report here: https://www.vlaanderen.be/building-living-and-energy/green-energy/heat map. For the year 2024, in addition to the existing heat networks, 19 locations could also be identified where plans are being made to build or expand a heat network. For these planned nets, the map layer also contains information about: name of the project, owner, postcode, municipality and whether or not a subsidy has been applied for under the call for green heat/residual heat. For more background information, please refer to the Heat Map report.

De 'Warmtekaart Vlaanderen 2019' werd in opdracht van het Vlaams Energie- en Klimaatagentschap opgesteld ter uitvoering van de EU-Richtlijn 2012/27/EU betreffende energie-efficiëntie en de Hernieuwbare Energie Richtlijn (EU) 2018/2001. De voornaamste producten zijn kaarten voor 2019 voor het grondgebied Vlaanderen met de warmtevraag van de grootverbruikers en kleinverbruikers, resultaten op niveau van de gemeenten en de statistische sectoren, kaarten van de bestaande en geplande warmtenetten en tenslotte ook locaties van potentiële leveringspunten van warmte. De studie werd uitgevoerd door VITO in samenwerking met de distributienetbeheerder Fluvius. Het bijhorende rapport kan u hier raadplegen: https://www.energiesparen.be/warmtekaart. Voor het jaar 2019 konden naast de bestaande warmtenetten ook 36 locaties geïdentificeerd worden waar men plannen heeft om een warmtenet aan te leggen of uit te breiden. Voor deze geplande netten bevat de kaartlaag ook info over: naam van het project, eigenaar, postcode, gemeente en of men al dan niet subsidie heeft aangevraagd in kader van de call groene warmte/restwarmte. Voor meer achtergrondinformatie wordt verwezen naar het rapport van de Warmtekaart.

Uncovering the Hidden Event Spaces of NYC Parks!

Mapping Event Locations, Accessibility, and Boroughs

By data.world's Admin [source]

About this dataset

Immerse yourself in NYC Parks events listings! This comprehensive dataset makes available the most recent records from 2013 and beyond, detailing information on events taking place in public parks throughout New York City. Beyond basic event data such as category, dates and times of activity, this dataset also offers further details such as organisers, labels, images associated with events or even YouTube video links related to them. Whether you are looking for a peaceful gathering hour or a thrilling outdoor adventure experience, this dataset provides you with all the necessary information on NYC Parks event listing!

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How to use the dataset

In this guide, we’ll walk you through how to use this dataset to find information about event spaces in NYC parks.

First off, if you have an idea of what type of event space or park you would like to explore then it will be helpful to use the search bar at the top left of the page. You can search for a specific park or city/borough name here. Clicking on any resulting options will bring up relevant information with regard to accessing an events space within that area.

If you’re looking for more general information about events spaces across NYC parks then scroll down and look at the summary table below which provides a brief description of all records in this dataset along with their related columns (e.g., Name, Latitude etc). The accessible column is particularly important as it tells users which areas are physically accessible while marking 'F' - as False otherwise indicating its not an easily accessible place within any given park/city/borough area covered by this dataset.

You can modify your query parameters by selecting columns listed on top interface shelf for further refining your results based on your unique needs (for example; if you need only those events spaces that are physically accessible). Data from multiple columns can also be combined together too making searching easier and accurate (for example; Brooklyn + nyc accessibility false filter) according to our research criteria needs through several combinations at once!

Finally clicking “Outer Join + Filter” button on top right side next above table takes user into advanced query editing mode – where further filtering is possible lets say if user wanted see particular boroughs having Location 1 OR address mentioning complete physical address lines without any postal codes- flexibility & accuracy here is endless too!

For more detailed instructions please refer our Data Documentation section –and Don't forget we have team member's ready 24 hours a day who are more than willing answer questions should one arise in need help anytime!. We invite everyone take part exploration beyond limits & let us know want like hear most loved ;) Happy exploring & discovering!

Research Ideas

• Creating map visualizations or heat maps to highlight event density in neighborhoods within the five boroughs of NYC.
• Analyzing trends over time of event categories within the different boroughs (e.g., how has the number of sports events increased/decreased in comparison to cultural events?).
• Generating dynamic reports that identify the most accessible NYC parks for those with mobility impairments and create easy-to-use indexes that can be used as a reference when organizing an outdoor activity or outing

Acknowledgements

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

License

See the dataset description for more information.

Columns

File: nyc-parks-events-listing-event-locations-1.csv | Column name | Description | |:---------------|:-------------------------------------------------------------------------------| | name | The name of the event. (String) | | lat | The latitude coordinate of the event location. (Float) | | long | The longitude coordinate of the event location. (Float) | | address | The address of the event location. (String) | | zip | The zip code of the event location. (...

This dataset contains tree canopy cover layers as derived and calculated via a land cover classification for the City of Mesa using 2024 MAG Imagery. The land cover classification utilized a Support Vector Machine Classifier and was calculated for various areas including city boundary, census tracts, census blocks, council district, etc.This dataset also contains the point locations and attributes of trees maintained by the City of Mesa. The point dataset was obtained by WCA from WCA in September 2024. The attributes of interest to this study included unique TreeID, Exact DBH, DBH Range, Height Range, Botanical Name, Common Name, Latitude, and Longitude. Updates to the tree layer were made by joining the results from the September 2024 i-Tree report. An i-Tree Eco Analysis was run in September 2024 using i-Tree Eco v6.0.38 and the results were joined based on unique tree ID to the Mesa’s tree inventory. Attributes added were: Structural Value ($), Carbon Storage (lb), Carbon Storage ($), Gross Carbon Sequestration (lb/yr), Gross Carbon Sequestration ($/yr), Avoided Runoff (cubicFT/yr), Avoided Runoff ($/yr), Pollution Removal (oz/yr), Pollution Removal ($/yr), Total Annual Benefits ($/yr), Height (ft), Canopy Cover (sqft), Tree Condition, Leaf Area (sqft), Leaf Biomass (lb), Leaf Area Index Basal Area (sqft), Cond, i-Tree_ID_BotName, i-Tree_ID_ComName and i-Tree_ID Genus. The exact definitions, meanings, calculations, etc. for the i-Tree Values can be found on i-Tree’s website via the i-Tree Eco User Manual. For certain layers the individual i-Tree values were aggregated by census tract, census block, zip code, etc. These results can be seen in the polygon layers with the following attribute values: CanopyCoverPer_Final, COUNT_Tree_ID, SUM_Replacement_Value_, SUM_Carbon_Storage_lb_, SUM_Carbon_Storage_, SUM_Gross_Carbon_Sequestration_lb_, SUM_Gross_Carbon_Sequestration_y, SUM_Avoided_Runoff_ftÂ_yr_, SUM_Avoided_Runoff_yr_, SUM_Pollution_Removal_oz_yr_, SUM_Pollution_Removal_yr_, and SUM_Total_Annual_Benefits_yr_

Spatial Mapping of HITECH Grants

Visualizing Health and Community Grant Locations in the U.S

By US Open Data Portal, data.gov [source]

About this dataset

This dataset provides crucial geographic data related to two of the U.S. Health Information Technology for Economic and Clinical Health (HITECH) Act programs: the Health IT Regional Extension Centers (REC) Program and the Beacon Communities Program. As part of the American Recovery and Reinvestment Act (ARRA), these grants were made available to provide citizens with access to health IT infrastructure investments throughout diverse communities across the United States. This crosswalk is an essential resource for anyone looking to link regional, state, county and zip code level program financials with performance metrics for visualization or comparison. With detailed information about region counties, codes, states, FIPS codes and ZIP codes associated with local HITECH grantees, this data presentation helps shed light on a financially impactful initiative from our federal government that can drastically improve healthcare delivery in thousands of cities nationwide!

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How to use the dataset

This dataset provides geographic data for the service areas of two of the HITECH programs, the Health IT Regional Extension Centers (REC) Program and the Beacon Communities Program. This can be used to map and visualize data related to those programs. Here is a guide on how to use this dataset:

• Get familiar with key columns: Familiarize yourself with the columns included in this dataset, including column names and descriptions for each column such as region, region_code, county_name, state_fips, county_fips and zip.
• Review data formats: If there are any discrepancies between your current format of data presented in this dataset versus what you may have currently in your system or within other sources of information - make sure to review those discrepancies prior exploring more from here onwards.
• Understand regional coverage: Refine the analysis by filtering out different grantee located based on specific regions or states - use necessary filters such as Region code or County FIPs code that will give you an easier view on which region/county grantee has been provided funding through these HHS programs as part of Hitech Act program distribution.
• Map & Visualize grantees: We can visualise geographically where are REC-Program & Beacon Communities Grants distributed across US by making a heatmap while taking desired geolocation coordinates like zip codes; query all available details under columns we need like zip codes along their respective countyp location & state value so that grants can be highlighted after it renders practical Map visuals for us giving an ease if further status / details required about entities who had taken these grants within certain area / regions!

Research Ideas

• Creating an interactive map to visualize grant program performance by region and county.
• Using the data to create a color-coded scatterplot graphic to show active grant program sites in the US.
• Generating reports on HITECH Grantee performance over time, grouped by geographic area or region

Acknowledgements

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

License

Unknown License - Please check the dataset description for more information.

Columns

File: healthit-dashboard-areatype-crosswalk-csv-1.csv | Column name | Description | |:------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------| | region | The region of the grantee. (String) | | region_code | The code for the region of the grantee. (String) | | county_name | The name of county where the grantee is located. (String) | | state_fips | The Federal Information Processing Standard (FIPS) code for knowledge of which state it is located in. (String) | | county_fips | The Federa...

Next tables present the detail description of the datasets developed in REACHOUT to characterize heat phenomena at city level by providing an assessment of the land surface temperature (heatmaps) of two European cities: Milan and Logroño. TECNALIA is the responsible partner for these datasets.

There is a wide range of methods that can be used to characterise the thermal behaviour of a city, each of them with its advantages and disadvantages. One of these methods uses the land surface temperature that is obtained from remote sensing observations. Although thermal indices are considered more suitable when characterising thermal comfort, still the LST can provide a useful information about the behaviour of a citiy’s surfaces and materials. This has implications for several applications such as urban energy efficiency or urban environmental health.

The input data used by the current version of the dataset came from Landsat 8. All the images acquired since 2013 by this satellite for Milan and Logroño were downloaded and processed to characterise not only the current (2019-2023) thermal behaviour of the city, but also its evolution considering the last seven 5-year windows.

- 2013-2017
- 2014-2018
- 2015-2019
- 2016-2020
- 2017-2021
- 2018-2022
- 2019-2023

The input data used in this dataset come from Landsat 8 downloaded from Earth Explorer (usgs.gov).

The format of this dataset is organized in two ZIP format files:
- LANDSAT_8_L2SP_000000-milan_LST_peak.zip
- LANDSAT_8_L2SP_000000-logrono_LST_peak.zip

Each of these zip files contain seven TIF images that represent the peak LST map according to the images of the above mentioned seven periods. The peak LST is obtained after getting the Annual Cycle Parameters of each of the periods and selecting a 30-day window centred on the day that the city reaches the maximum LST.

The values of the images are in degree Celsius and nodata value is -9999.

This submission includes a fault map of the Oregon Cascades and backarc, a probability map of heat flow, and a fault density probability layer. More extensive metadata can be found within each zip file.

For information about "Oregon Faults," contact John David Trimble, Oregon State University. trimbljo@onid.oregonstate.edu This is a probability map of heat flow in the Cascades in Oregon where a threshold of 80 wM/m squared was used. Probability was calculated using simple kriging with declustering. The data used for this statistical surface were down-loaded from the Southern Methodist University Geothermal Laboratory.

Heat makes up around half of all the energy we use and so efficient heat management is a major national focus for tackling fuel poverty, meeting environmental challenges and supporting our economy , for more background see Scotland's Heat Map (and the related item tab). Total annual energy consumption (kWh) and 5 year average for the corrected usage for open buildings in GCC estate from 2008 to 2012 .The fuel can be gas, electricity, gas-oil or coal and the year is calendar year. Location covers address, postcode and UPRN (Notes: except air quality units & heat pumps; also Cathkin landfill which is located outside GCC boundary has been assigned a temporary UPRN).For properties with 2 heating sources e.g. Daldowie Training Centre, only the primary source has been included. Data supplied: 2014-03-21 by Glasgow District Council Licence: None

This geodatabase was built to cover several geothermal targets developed by Flint Geothermal in 2012 during a search for high-temperature systems that could be exploited for electric power development. Several of the thermal springs and wells in the Routt Hot Spring and Steamboat Springs areahave geochemistry and geothermometry values indicative of high-temperature systems.

Datasets include:

1. Results of reconnaissance shallow (2 meter) temperature surveys
2. Air photo lineaments
3. Groundwater geochemistry
4. Georeferenced geologic map of Routt County
5. Various 1:24,000 scale topographic maps Data is in UTM Zone 13 NAD27 projection. The datasets in the geodatabase are a mixture of public domain data as well as data collected by Flint Geothermal, now being made public. It is assumed that the user has internet access, for the mxd file accesses ESRI's GIS servers. This is an ESRI geodatabase version 10, together with an ESRI MXD file version 10.2.

Interpolated maps of heat flow, temperature gradient, and quartz geothermometers are included as TIF files. Zones of critical stress map is also included as a TIF file. The zones are given a 5km diameter buffer. The study area is only a part of the Basin and Range, but it does includes the Tularosa Basin. Heat flow data points were extrapolated to create the TIF image within the zipped file. Heat flow values are in mW/m^2

We analyzed each zip code for the severity of heat exposure, the prevalence of sensitive groups, and the area’s ability to adapt to heat. These variables were then correlated with publicly available emergency room and hospitalization rate data from 2015-2021 to create two separate heat vulnerability indices. The two heat vulnerability maps were then averaged to create Average Heat Vulnerability.

Location of seismic lines carried out under DOE funded project Advanced Seismic Data Analysis Program (The Hot Pot Project). ArcGIS map package containing topographic base map, Township and Range layer, Oski BLM and private leases at time of survey, and locations, with selected shot points, of the five seismic lines. ArcGIS map file containing the Seismic Line Location Map

Download: https://s3-ant1.antwerpen.be/prd-open-data-data-tank/raster/Hittekaart_dag.zip - https://s3-ant1.antwerpen.be/prd-open-data-tank/raster/Hittekaart_dag.zip Heatmaps SW/EMA Der städtische Wärmeinseleffekt (SHE) tritt auf, wenn es einen Temperaturunterschied zwischen Stadt und umliegender Landschaft gibt und ist hauptsächlich das Ergebnis der bebauten und gepflasterten Umgebung. Die SHE ist nachts die größte und macht sich besonders in den Sommermonaten bemerkbar. Die bekannteste Folge dieses Effekts ist der „Wärmestress“ oder das „Unwohlsein“ bei heißen Temperaturen. Während Hitzewellen und insbesondere nachts kann Hitzestress zu gesundheitlichen Problemen und sogar zur Sterblichkeit führen, insbesondere bei kranken, jungen (-4y) und älteren (+65y) Bevölkerungsgruppen. Weitere Folgen der SHE sind das Versagen von Infrastruktur, Maschinen, die Verringerung der Arbeitsproduktivität, die reduzierte Lebensdauer von Materialien... Die Faktoren hinter dieser Strahlungstemperaturkarte und die somit Einfluss auf die SHE sind: Schatteneffekte, Verdunstung, Windeffekte und Wärmespeicherung und Strahlung von Gebäuden und Materialien. Bisher gibt es keine konkreten Ziele oder Regelungen in Bezug auf die SHE. Mögliche Maßnahmen, die die SHE mildern können, sind jedoch: Schaffung von maximalem Schatten, minimalem Pflaster, natürlichen Lüftungskorridoren, Freiwasseranlagen, grüner Gestaltung und Bäumen im öffentlichen Raum, begrünten Dächern, grünen Fassaden, hellen Materialien für Gebäude und den öffentlichen Raum und Verringerung des „Sky-View-Faktors“ oder der „Gebäudeeindämmung“ des öffentlichen Raums, um die indirekte Strahlung von Materialien zu vermeiden. Die Stadt Antwerpen kartierte die SHE zunächst, um eine Präventions- und Kommunikationspolitik für ihre Bewohner, Unternehmen und Besucher zu entwickeln und politische Maßnahmen zur Steigerung der Lebensqualität in der Stadt umzusetzen. Im zweiten Fall ist diese Forschung Teil der Vorbereitung der Antwerpener Anpassungsstrategie, die systematisch die Herausforderungen der Stadt im Hinblick auf den Klimawandel abbildet. Die gleiche Studie ergab, dass die SHE als Folge der globalen Erwärmung zunehmen wird.

In this submission is the groundwater composite risk segment (CRS) used for play fairway analysis. Also included is a heat flow probability map, and a shaded relief map of the Tularosa Basin, NM. Heat flow map of the Tularosa Basin, New Mexico and Texas, vectorized from the SMU 2011 Geothermal Laboratory Heat Flow Map of the Conterminous United States (linked in this submission)

This submission includes composite risk segment models in raster format for permeability, heat of the earth, and MT, as well as the final PFA model of geothermal exploration risk in Southwestern Utah, USA. Additionally, this submission has data regarding hydrothermally altered areas, and opal sinter deposits in the study area. All of this information lends to the understanding and exploration for hidden geothermal systems in the area. This dataset represents the integration of heat data, including heat flow and Na/K geothermometer data, which was used in the Southeastern Great Basin play fairway analysis project, Phase 2, as a composite risk segment. The input data were probability based so this dataset has been normalized between 0 and 1 for use in PFA.

Neal Hot Springs-ESRI Geodatabase (ArcGeology v1.3): - Contains all the geologic map data, including faults, contacts, folds, unit polygons, and attitudes of strata and faults. - List of stratigraphic units and stratigraphic correlation diagram. - Three cross-sections. - Locations of production, injection, and exploration wells. - Locations of 40Ar/39Ar samples. - Location of XRF geochemical samples. - 3D model constructed with EarthVision using geologic map data, cross-sections, drill-hole data, and geophysics (model not in the ESRI geodatabase). Contains all the geologic map data, including faults, contacts, folds, unit polygons, and attitudes of strata and faults. May require ArcGIS to open.

Retirement Notice: This item is in mature support as of June 2023 and will be retired in December 2025. A replacement item has not been identified at this time. Esri recommends updating your maps and apps to phase out use of this item.This map shows the total crime index in the U.S. in 2022 in a multi-scale map (by state, county, ZIP Code, tract, and block group). The layer uses 2020 Census boundaries. The pop-up is configured to include the following information for each geography level:Total crime indexPersonal and Property crime indices Sub-categories of personal and property crime indices Permitted use of this data is covered in the DATA section of the EsriMaster Agreement (E204CW) and these supplemental terms.