According to a January 2022 analysis of the leading dating apps downloaded from the Apple App Store, the Badoo mobile app was indexed as collecting the largest number of data types from its users' activity. Bumble and HER ranked second, with an index value of close to 68, respectively. Grindr had a reported index value of 62, while market leader Tinder was indexed with a value of 38.4.

This dataset provides detail about the number of times users have viewed datasets on data.kingcounty.gov.

User activity is provided by date, asset uid, asset type, asset name, access type and user segment. Please see Site Analytics: Asset Access for more detail about these fields.

The dataset will reflect new Asset Access records within a day of when they occur.

The Qld Wildlife API is being replaced

Please note that the Qld Wildlife API will soon be replaced with a new API. The new API is currently planned to be released in May 2025. Please come back to this webpage for updates. If you have any questions or feedback, please email wildnet@des.qld.gov.au

This API accesses data from QLD Government's WildNet database that has been approved for public release. There are a number of functions that retrieve species names, profiles, notes, statuses, images, species survey locations and project information.

Please see https://apps.des.qld.gov.au/species for more information on using the API functions.

Data can be retrieved in 3 different formats by adding the format variable to the end of the url (e.g. &f=xml). The default format is json if the f (format) variable is omitted.
E.g.
- JSON: https://apps.des.qld.gov.au/species/?op=getkingdomnames&f=json
- XML: https://apps.des.qld.gov.au/species/?op=getkingdomnames&f=xml
- CSV: https://apps.des.qld.gov.au/species/?op=getkingdomnames&f=csv

When spatial locations are returned, GeoJSON or KML will be used when requesting the json and xml formats.

Species profile search can be used to locate species information (by name or a taxonomy search). It uses the Get species by ID function to display species profiles with images and maps and uses the Get surveys by species function for downloading data.

Biomaps provides a map interface to display the WildNet records approved for publication with other spatial layers (such as cadastre, protected areas, vegetation and biodiversity value mapping). A range of WildNet species list reports based on all WildNet records and other environmental reports can be requested for properties and drawn areas etc.

WetlandMaps provides a map interface to display WildNet records approved for publication with other spatial layers (such as wetland mapping).

The Queensland Globe can be used to access WildNet records approved release and access summarised WildNet data in 10x10km grids.

Other WildNet products are made available via the Queensland Government Open Data Portal.

The resources listed below are the service endpoints for each of the operations (or functions) available.

Available variables
f: Format - Setting the 'f' variable will determine the format of the response. There are 4 possible options; json, xml, kml and csv. Json is the default if 'f' is not set. If the output is spatial, GeoJson will be return for 'f=json' and KML will be returned for 'f=xml' or 'f=kml'.
projids: Project Ids - Comma separated list of project ids. Use Get projects to access project IDs.
projtitle: Project Title - A title (full or partial) that is used as a search string to search for a project or projects.
proj: Include Project Details - This indicates if the project details are to be included in the output. The default is true.
org: Organisation ID - An ID that is associated with an organisation. Use Get organisations to access organisation IDs.
bbox: Bounding Box - A bounding box that defines a geographical area. Specified as top left, bottom right, e.g. latitude,longitude,latitude,longitude.
circle: Circle - A circle (buffered point) that defines a geographical area. It is specified as a centroid and a radius (metres), e.g. latitude,longitude,distance.
pagecount: Page Count - The number of records to return on a page.
pageindex: Page Index - The page index to return.
p: Location Precision - The distance in metres that indicates the accuracy of the records location.
min: Minimum Start Date - The earliest date for a record to be returned.
max: Maximum Start Date - The latest date for a record to be returned.
kingdom: Kingdom - A kingdom's common name.
class: Class - A class scientific name.
classes: Classes - A comma separated list of class scientific names.
family: Family - A scientific family name.
species: Species Name - A scientific species name.
taxonid: Taxon ID - A unique id that identifies a particular species. Use Species search to access taxonids for particular species.

CAL FIRE's Fire and Resource Assessment Program (FRAP) annually maintains and distributes an historical fire perimeter dataset from across public and private lands in California. The GIS data is developed with the cooperation of the United States Forest Service Region 5, the Bureau of Land Management, the National Park Service and the United States Fish and Wildlife Service and is released in the spring with added data from the previous calendar year. Although the dataset represents the most complete digital record of fire perimeters in California, it is still incomplete, and users should be cautious when drawing conclusions based on the data.

This app contains three pages of maps and documentation of the historical fire perimeter metadata:

Historical Fire Perimeters: The landing page highlights the recent large fires (≥5,000 acres) on a backdrop of all of the dataset's documented fire perimeters dating back to 1878. This map includes perimeters symbolized by decade, county boundaries, California Vegetation, and NAIP imagery back to 2005. This page provides users the ability to add their own data or filter the fire perimeter data. It cleanly lists fire perimeters shown on the map with their name, year, and GIS calculated acreage. The user can navigate to the CAL FIRE current incident webpage or provide comments to the dataset's steward.

Times Burned: The second page provides a map showing an analysis performed annually on the fire perimeter dataset to show case burn frequency from 1950 to present for fires greater than one acre.

Fire Across Time: This third page provides a time enabled layer of the fire perimeter dataset, featuring a time slider to allow users to view the perimeter dataset across time.

The final page provides the user with the dataset's metadata, including its most current data dictionary.

For any questions, please contact the data steward:

Kim Wallin, GIS Specialist

CAL FIRE, Fire & Resource Assessment Program (FRAP)

kimberly.wallin@fire.ca.gov

Summary information about locations of environmental monitoring sites that have monitoring data publicly available. Types of monitoring sites are air quality, water quality, storm tides, wave heights and direction. Each site provides links to download its data and to its associated webpage if it exists.

Field descriptions
Monitoring type: The type of monitoring being conducted at that location
Site name: The name of the site
Latitude: The latitude in decimal degrees
Longitude: The longitude in decimal degrees
Resource label: The name of the resource (data file) that is available for download
Start date: First date of the monitoring for that resource
End date: Last date of the monitoring for that resource
Near real-time period: If the resource contains near real-time data, this field indicates the numerical length of the period
Period type: If the resource contains near real-time data, this field indicates the type of period, e.g. day, current year, etc
Update frequency: Indicates how often the resource is updated
Resource Url: The location of the resource to download the data
Website Url: The location of the webpage associated with this site, if it exists

The National Flood Hazard Layer (NFHL) data incorporates all Digital Flood Insurance Rate Map(DFIRM) databases published by FEMA, and any Letters Of Map Revision (LOMRs) that have been issued against those databases since their publication date. The DFIRM Database is the digital, geospatial version of the flood hazard information shown on the published paper Flood Insurance Rate Maps(FIRMs). The primary risk classifications used are the 1-percent-annual-chance flood event, the 0.2-percent-annual-chance flood event, and areas of minimal flood risk. The NFHL data are derived from Flood Insurance Studies (FISs), previously published Flood Insurance Rate Maps (FIRMs), flood hazard analyses performed in support of the FISs and FIRMs, and new mapping data where available. The FISs and FIRMs are published by the Federal Emergency Management Agency (FEMA). The specifications for the horizontal control of DFIRM data are consistent with those required for mapping at a scale of 1:12,000. The NFHL data contain layers in the Standard DFIRM datasets except for S_Label_Pt and S_Label_Ld. The NFHL is available as State or US Territory data sets. Each State or Territory data set consists of all DFIRMs and corresponding LOMRs available on the publication date of the data set.

According to the United Nations, 54% of the world’s population resides in urban areas in the year 2014. It is projected that by 2050 this number will increase by 12%. The direct effect of this urban drift has had profound effects on social, economic and ecological systems, causing stresses on the environment and society. The social and economic implications include impacts from human activities such as transport, industrialization, combustion, construction etc., all of which have a direct or indirect bearing on the environment. These pollution sources have led to release of pollutants such as Nitrogen dioxide (NO2), Particulate Matter (PM) and Sulphur dioxide (SO2) into the atmosphere. It is believed that air pollution is influenced by urban dynamics.In this project, we present a method for predicting historical air quality (as measured by daily median PM25 concentration) for locations where no ground-based sensors are present, by using weather data and remote sensing data from sources like the Sentinel 5P satellite. Air quality data is obtained for 555 cities and supplemented by satellite and weather data. This is then used to build a model to predict the air quality for a given date and location. A competition hosted by Zindi was used to crowd-source the creation of the model used, with the winning code forming the basis of our modelling approach.We use the trained model to create a new dataset of historical air quality predictions for cities across Africa, available at https://github.com/johnowhitaker/air_quality_prediction. For access to the original data see https://search.datacite.org/works/10.15493/sarva.301020-2.

We seek to mitigate the challenges with web-scraped and off-the-shelf POI data, and provide tailored, complete, and manually verified datasets with Geolancer. Our goal is to help represent the physical world accurately for applications and services dependent on precise POI data, and offer a reliable basis for geospatial analysis and intelligence.

Our POI database is powered by our proprietary POI collection and verification platform, Geolancer, which provides manually verified, authentic, accurate, and up-to-date POI datasets.

Enrich your geospatial applications with a contextual layer of comprehensive and actionable information on landmarks, key features, business areas, and many more granular, on-demand attributes. We offer on-demand data collection and verification services that fit unique use cases and business requirements. Using our advanced data acquisition techniques, we build and offer tailormade POI datasets. Combined with our expertise in location data solutions, we can be a holistic data partner for our customers.

KEY FEATURES - Our proprietary, industry-leading manual verification platform Geolancer delivers up-to-date, authentic data points

• POI-as-a-Service with on-demand verification and collection in 170+ countries leveraging our network of 1M+ contributors

• Customise your feed by specific refresh rate, location, country, category, and brand based on your specific needs

• Data Noise Filtering Algorithms normalise and de-dupe POI data that is ready for analysis with minimal preparation

DATA QUALITY

Quadrant’s POI data are manually collected and verified by Geolancers. Our network of freelancers, maps cities and neighborhoods adding and updating POIs on our proprietary app Geolancer on their smartphone. Compared to other methods, this process guarantees accuracy and promises a healthy stream of POI data. This method of data collection also steers clear of infringement on users’ privacy and sale of their location data. These purpose-built apps do not store, collect, or share any data other than the physical location (without tying context back to an actual human being and their mobile device).

USE CASES

The main goal of POI data is to identify a place of interest, establish its accurate location, and help businesses understand the happenings around that place to make better, well-informed decisions. POI can be essential in assessing competition, improving operational efficiency, planning the expansion of your business, and more.

It can be used by businesses to power their apps and platforms for last-mile delivery, navigation, mapping, logistics, and more. Combined with mobility data, POI data can be employed by retail outlets to monitor traffic to one of their sites or of their competitors. Logistics businesses can save costs and improve customer experience with accurate address data. Real estate companies use POI data for site selection and project planning based on market potential. Governments can use POI data to enforce regulations, monitor public health and well-being, plan public infrastructure and services, and more. A few common and widespread use cases of POI data are:

• Navigation and mapping for digital marketplaces and apps.
• Logistics for online shopping, food delivery, last-mile delivery, and more.
• Improving operational efficiency for rideshare and transportation platforms.
• Demographic and human mobility studies for market consumption and competitive analysis.
• Market assessment, site selection, and business expansion.
• Disaster management and urban mapping for public welfare.
• Advertising and marketing deployment and ROI assessment.
• Real-estate mapping for online sales and renting platforms.About Geolancer

ABOUT GEOLANCER

Quadrant's POI-as-a-Service is powered by Geolancer, our industry-leading manual verification project. Geolancers, equipped with a smartphone running our proprietary app, manually add and verify POI data points, ensuring accuracy and authenticity. Geolancer helps data buyers acquire data with the update frequency suited for their specific use case.

A subset of the Mobile Home Parks: Last Inspection dataset, only showing locations with less than 51 sites. This dataset includes the name and location of active mobile home parks operating in New York State. Active mobile home parks include only parks that were categorized as active (i.e., operating with accommodations for the placement of five or more mobile or manufactured homes) on the date the data was downloaded from a Department of Health database. This data also includes the date of the last inspection and violations of Part 17 of the New York State Code of Rules and Regulations that were identified during that inspection. Additionally, the data includes the park owner-operator, the number of sites within the park, the type of on-site water source and sewage disposal system serving the mobile home park, and whether a pool or beach is operated as part of the mobile home park. The location of the mobile home park includes its street address, city, state, zip code, municipality, and county.

A data set of land use between June 2000 and June 2007 for New South Wales. Land use is classified to three separate classification schemes. These classification schemes are:3

• NSW Land Use Mapping Program (LUMAP).
• NSW SCALD (Standard Classification for Attributes of Land) Classification
• ALUM (Australian Land Use and Management) Classification.

The LUMAP Classification is DECC's most recent classification for mapping of land use classes for NSW. It is a simple numeric classification, open-ended to enable additional classes to be added.

Prior to LUMAP, the SCALD classification was the standard for mapping of land use in NSW. It is a combined alpha-numeric classification system.

The ALUM classification is based upon the modified Baxter & Russell classification and presented according to the specifications contained in http://adl.brs.gov.au/mapserv/landuse/index.cfm?fa=app.ALUMClassification.

Version 6 of the classification describes the land use classes. Earlier copies of the data set may have used Versions 4 or 5.

The mapping was commenced in April 2001 and completed by June 2007. The date of the data set is set as the land use occurring at the time the satellite imagery was acquired, which can range from 1999 to 2006. This dataset was updated in May 2011 to include values in the vacant attribute fields of Source, Source Date, Source Scale, Reliability and LU Mapping Date.

This statistic shows the different ways used to cease all communication and contact with somebody French dating app users stated they have faced with a person they have met on a dating site, in 2018. It appears that 55 percent of the respondents had already being "ghosted", which means that the person they were in contact with ended suddenly all communications without any explanation.