This dataset is a compilation of address point data for the City of Tempe. The dataset contains a point location, the official address (as defined by The Building Safety Division of Community Development) for all occupiable units and any other official addresses in the City. There are several additional attributes that may be populated for an address, but they may not be populated for every address. Contact: Lynn Flaaen-Hanna, Development Services Specialist Contact E-mail Link: Map that Lets You Explore and Export Address Data Data Source: The initial dataset was created by combining several datasets and then reviewing the information to remove duplicates and identify errors. This published dataset is the system of record for Tempe addresses going forward, with the address information being created and maintained by The Building Safety Division of Community Development.Data Source Type: ESRI ArcGIS Enterprise GeodatabasePreparation Method: N/APublish Frequency: WeeklyPublish Method: AutomaticData Dictionary

Updated Continually

The Reach Address Database (RAD) stores the reach address of each Water Program feature that has been linked to the underlying surface water features (streams, lakes, etc) in the National Hydrology Database (NHD). (A reach is the portion of a stream between two points of confluence. A confluence is the location where two or more streams flow together.)

This map shows site address points in the City of San José and provides tools to select and download address data. Addresses are part of the City's Master Address Database (MAD), which is a comprehensive database containing authoritative physical/site addresses. Mailing addresses may differ from site addresses.This map was created for the City of San José public maps gallery. The maps gallery is a collection of maps created and maintained by the Enterprise GIS team. Maps gallery maps showcase programs, projects, and spatial information derived from City data. The information in these maps is publicly shared for the purpose of transparency and accessibility. Many maps from the maps gallery are also embedded with the City website. Much of the data that supports these maps can be directly downloaded from the Open GIS Data Portal. City of San Jose Website: https://www.sanjoseca.gov/City of San Jose Maps Gallery: https://gis.sanjoseca.gov/apps/mapsgallery/City of San Jose Open GIS Data Portal: https://gisdata-csj.opendata.arcgis.com/

Welcome to Apiscrapy, your ultimate destination for comprehensive location-based intelligence. As an AI-driven web scraping and automation platform, Apiscrapy excels in converting raw web data into polished, ready-to-use data APIs. With a unique capability to collect Google Address Data, Google Address API, Google Location API, Google Map, and Google Location Data with 100% accuracy, we redefine possibilities in location intelligence.

Key Features:

Unparalleled Data Variety: Apiscrapy offers a diverse range of address-related datasets, including Google Address Data and Google Location Data. Whether you seek B2B address data or detailed insights for various industries, we cover it all.

Integration with Google Address API: Seamlessly integrate our datasets with the powerful Google Address API. This collaboration ensures not just accessibility but a robust combination that amplifies the precision of your location-based insights.

Business Location Precision: Experience a new level of precision in business decision-making with our address data. Apiscrapy delivers accurate and up-to-date business locations, enhancing your strategic planning and expansion efforts.

Tailored B2B Marketing: Customize your B2B marketing strategies with precision using our detailed B2B address data. Target specific geographic areas, refine your approach, and maximize the impact of your marketing efforts.

Use Cases:

Location-Based Services: Companies use Google Address Data to provide location-based services such as navigation, local search, and location-aware advertisements.

Logistics and Transportation: Logistics companies utilize Google Address Data for route optimization, fleet management, and delivery tracking.

E-commerce: Online retailers integrate address autocomplete features powered by Google Address Data to simplify the checkout process and ensure accurate delivery addresses.

Real Estate: Real estate agents and property websites leverage Google Address Data to provide accurate property listings, neighborhood information, and proximity to amenities.

Urban Planning and Development: City planners and developers utilize Google Address Data to analyze population density, traffic patterns, and infrastructure needs for urban planning and development projects.

Market Analysis: Businesses use Google Address Data for market analysis, including identifying target demographics, analyzing competitor locations, and selecting optimal locations for new stores or offices.

Geographic Information Systems (GIS): GIS professionals use Google Address Data as a foundational layer for mapping and spatial analysis in fields such as environmental science, public health, and natural resource management.

Government Services: Government agencies utilize Google Address Data for census enumeration, voter registration, tax assessment, and planning public infrastructure projects.

Tourism and Hospitality: Travel agencies, hotels, and tourism websites incorporate Google Address Data to provide location-based recommendations, itinerary planning, and booking services for travelers.

Discover the difference with Apiscrapy – where accuracy meets diversity in address-related datasets, including Google Address Data, Google Address API, Google Location API, and more. Redefine your approach to location intelligence and make data-driven decisions with confidence. Revolutionize your business strategies today!

This is the domain table for Address Points (designated by the Chief Data Officer (CDO) per Mayor’s Order 2017-115). Districtwide domain tables are database tables designated by the CDO to provide a standard source of values to be used across District information systems and data transformations, as defined by the Open Data Policy. The source table for thisis the DCGIS.AddressPtlayer.https://octo.dc.gov/page/district-columbia-data-policy

This dataset comes from the Open National Address Base project initiated by OpenStreetMap France.

For more information on this project: http://openstreetmap.fr/blogs/cquest/bano-banco

Origin of data

• OpenStreetMap
• data available in opendata
• address data collected on the cadastral site (source DGFiP 2014)

Distribution format

These files are available in shapefile format, in WGS84 projection (EPSG :4326) as well as in CSV format and experimentally as github project.

Description of content

For each address:

• id (unique): code_insee + codefantoir + number
• number: street number with suffix (e.g.: 1, 1BIS, 1D)
• street: street name
• post_code: 5-character postcode
• city: name of the municipality
• source: OSM = data directly from OpenStreetMap, OD = data from local opendata sources, CAD = data directly from the cadastre, C+O = cadastre data enriched by OSM (road name for example)
• lat: latitude in WGS84 decimal degrees
• lon: longitude in WGS84 decimal degrees

updates, corrections

To update and correct BANO data, simply make improvements directly in OpenStreetMap, they will be taken into account in the next update cycle.

A one-stop collaborative reporting/correction window will soon be set up to simplify the process of improving the content of the database. To participate in its co-construction, do not hesitate to contact us!

For any questions concerning the project or this dataset, you can contact bano@openstreetmap.fr

Address Creation, LLC Whois Database, discover comprehensive ownership details, registration dates, and more for Address Creation, LLC with Whois Data Center.

Our address datasets contain all geospatial address data of Portugal. You can use this data to send direct mail campaigns to households within a certain radius of your store, or to limit your online campaigns to viewers within a specific catchment area.

Address-Level Targeting in Portugal

Spotzi users can also combine our address data with consumer demographics and behavior data - such as insights into purchasing habits or disposable income - to ensure that every campaign targets their best-fit customers.

A global self-hosted Market Research dataset containing all administrative divisions, cities, addresses, and zip codes for 247 countries. All geospatial data is updated weekly to maintain the highest data quality, including challenging countries such as China, Brazil, Russia, and the United Kingdom.

Use cases for the Global Zip Code Database (Market Research data)

• Address capture and validation

• Map and visualization

• Reporting and Business Intelligence (BI)

• Master Data Mangement

• Logistics and Supply Chain Management

• Sales and Marketing

Data export methodology

Our map data packages are offered in variable formats, including .csv. All geographic data are optimized for seamless integration with popular systems like Esri ArcGIS, Snowflake, QGIS, and more.

Product Features

• Fully and accurately geocoded

• Administrative areas with a level range of 0-4

• Multi-language support including address names in local and foreign languages

• Comprehensive city definitions across countries

For additional insights, you can combine the map data with:

• UNLOCODE and IATA codes

• Time zones and Daylight Saving Times

Why do companies choose our Market Research databases

• Enterprise-grade service

• Reduce integration time and cost by 30%

• Weekly updates for the highest quality

Note: Custom geographic data packages are available. Please submit a request via the above contact button for more details.

The pathway representation consists of segments and intersection elements. A segment is a linear graphic element that represents a continuous physical travel path terminated by path end (dead end) or physical intersection with other travel paths. Segments have one street name, one address range and one set of segment characteristics. A segment may have none or multiple alias street names. Segment types included are Freeways, Highways, Streets, Alleys (named only), Railroads, Walkways, and Bike lanes. SNDSEG_PV is a linear feature class representing the SND Segment Feature, with attributes for Street name, Address Range, Alias Street name and segment Characteristics objects. Part of the Address Range and all of Street name objects are logically shared with the Discrete Address Point-Master Address File layer.

This dataset is an enrichment of the INSEE dataset (SIRENE database of enterprises and their establishments (SIREN, SIRET)) (https://www.data.gouv.fr/en/datasets/base-sirene-des-entreprises-et-de-leurs-etablissements-siren-siret/). This enriches the original base as follows: - Breakdown of the StockEstablishment file by geographical grid: departments and municipalities. - Addition of a number of columns relating to the geolocation of establishments based on the most relevant proximity score between the address indicated in the SIRENE database and the National Address Database or the Points of Interest of Openstreetmap. - Longitude field: longitude of the establishment - Field "latitude": Latitude of establishment - Field geo_score: Trust score returned by the addok geocoder (between 0 and 1, the higher the score, the more relevant the geocoding seems) - Field geo_type: type of address found - Field geo_address: wording of the address found - Field geo_id: identifier of this address in the source database where it was found (BAN or POI) - Field geo_line: which address line of the SIRENE database could be geocoded (G=geographical, D=declared, N=normalized) - Field geo_l4: line 4 to standard AFNOR address - Field geo_l5: line 5 to standard AFNOR address The processing allowing the production of this dataset is carried out by Etalab. It is largely inspired by the previous work of Christian Quest available here. This processing is based on the geocoder Addok. This dataset is used in the search engine of the business directory and in its API (https://api.gouv.fr/les-api/api-recherche-entreprises).

The AIMS: Address data table contains details relating to the creation and updating of an address including the version of an address and is part of the AIMS: Street Address.

Refer to the NZ Addresses Data Dictionary for detailed metadata and information about this dataset.

Background

This dataset provides all allocated addresses as advised to Toitū Te Whenua LINZ by Territorial Authorities (TAs). Under the Local Government Act 1974 (section 319) it is the responsibility of the TAs to advise the Surveyor-General at Toitū Te Whenua LINZ of all allocated addresses in their district.

The dataset is maintained by Toitū Te Whenua LINZ in the Address Information Management System (AIMS) which is centralised database for the management of national addresses, including for electoral purposes. This dataset is updated weekly on the LINZ Data Service.

For a simplified version of the data contained within these tables see NZ Addresses

Connecticut address point dataset used for locating 9-1-1 calls. The address point feature class format is derived from National Emergency Number Association (NENA) and Federal Geographic Data Committee (FGDC) addressing standards. All address components, like address number, streets name and unit number, are broken up into their individual components to enable maximum flexibility for use. Fields within feature class should be able to accommodate all addresses within the state of Connecticut. The source for the addresses is primarily derived from municipal parcel data and other municipal sources.

The US Consumer Email file has 700 million+ emails with new emails monthly. Emails are stored in plain text, MD5, SHA 256, and SHA1 formats. Additionally, our email recency validation processing can increase the success of your email campaigns by as much as 4X.

We have developed this file to be tied to our Consumer Demographics Database so additional demographics can be applied as needed. Each record is ranked by confidence and only the highest quality data is used.

Note - all Consumer packages can include necessary PII (address, email, phone, DOB, etc.) for merging, linking, and activation of the data.

BIGDBM Privacy Policy: https://bigdbm.com/privacy.html

Address points were developed to supplement the address information supplied by the CSCL centerline. Some computer aided dispatch systems use address points as the primary source for locating an address. For additional information and resources, please visit https://nycmaps-nyc.hub.arcgis.com/datasets/nyc::address-point/about

This dataset serves as a lookup table to determine if environmental records exist in a Chicago Department of Public Health (CDPH) environmental dataset for a given address.
Data fields requiring description are detailed below. MAPPED LOCATION: Contains the address, city, state and latitude/longitude coordinates of the facility. In instances where the facility address is a range, the lower number (the value in the “Street Number From” column) is used. For example, for the range address 1000-1005 S Wabash Ave, the Mapped Location would be 1000 S Wabash Ave. The latitude/longitude coordinate is determined through the Chicago Open Data Portal’s geocoding process. Addresses that fail to geocode are assigned the coordinates 41.88415000022252°, -87.63241000012124°.This coordinate is located approximately just south of the intersection of W Randolph and N LaSalle. COMPLAINTS: A ‘Y’ indicates that one or more records exist in the CDPH Environmental Complaints dataset. NESHAPS & DEMOLITON NOTICES: A ‘Y’ indicates that one or more records exist in the CDPH Asbestos and Demolition Notification dataset. ENFORCEMENT: A ‘Y’ indicates that one or more records exist in the CDPH Environmental Enforcement dataset. INSPECTIONS: A ‘Y’ indicates that one or more records exist in the CDPH Environmental Inspections dataset. PERMITS: A ‘Y’ indicates that one or more records exist in the CDPH Environmental Permits dataset. TANKS: A ‘Y’ indicates that one or more records exist in the CDPH Storage Tanks dataset. Each 'Y' is a clickable link that will download the corresponding records in CSV format.

Address Residential Units. This dataset contains residential units and attributes of Address points, created as part of the Master Address Repository (MAR) for the D.C. Residential units can be condominiums or also apartments. Office of the Chief Technology Officer (OCTO) and DC Department of Consumer and Regulatory Affairs . It contains the addresses in the District of Columbia which are typically placed on the buildings. More information on the MAR can be found at https://opendata.dc.gov/pages/addressing-in-dc.

This dataset is from the City of Boston's Street Address Management (SAM) system, containing Boston addresses. Updated nightly and shared publicly.

This dataset contains street centerlines for vehicular and foot traffic in Allegheny County.

Street Centerlines are classified as Primary Road, Secondary Road, Unpaved Road, Limited Access Road, Connecting Road, Jeep Trail, Walkway, Stairway, Alleyway and Unknown. A Primary Road is a street paved with either concrete or asphalt that has two (2) or more lanes in each direction. A Secondary Road is a residential type hard surface road, or any hard surface road with only one (1) lane in each direction. An Unpaved Road is any road covered with packed dirt or gravel. A Limited Access Road is one that can only be accessed from a Connecting Road such as an Interstate Highway. A Connecting Road is a ramp connecting a Limited Access Road to a surface street. A Walkway is a paved or unpaved foot track that connects two (2) roads together. Walkways within College Campuses will also be shown. Recreational pedestrian trails and walkways through parks and wooded areas are not considered transportation and will not be digitized during this update. Walkways will not have an Edge of Pavement feature. A Stairway is a paved or wooden structure that connects two (2) roads together. Recreational pedestrian trails and walkways through parks and wooded areas are not considered transportation and will not be digitized during this update. An Alleyway is a road, usually narrower than a Secondary Road that runs between, but parallel to, two (2) Secondary Roads. Generally, Outbuildings will be adjacent to Alleyways. A Jeep Trail is a vehicular trail used for recreation. A Jeep Trail will not have an associated edge of pavement feature. A road coded as Unknown is a road, which in the judgment of the photogrammetrist, does not fall into any of the categories listed.

Centerlines will be visually placed between the edges of pavement. One (1) centerline will be placed between each edge of pavement. Roads with medial strips, such as Limited Access Roads, will have two (2) centerlines for those portions of the road where the medial strip is present. For roads that terminate with a cul-de-sac, the centerline shall continue through the center of the cul-de-sac and stop at the edge of pavement.

All attribute data will remain for all Street Centerlines that are not updated. For Street Centerlines that are new, the only attribute field that will be populated is the FeatureCode and UPDATE_YEAR. If a Street Centerline is graphically modified, the existing attribute data will remain and the UPDATE_YEAR will be set to 2004. The attribute values for 2004 Street Centerlines should be considered suspicious until verified.

The ArcInfo Street Centerline coverage that is being updated has 800 segments of Paper Streets, 66 segments of Vacated Streets and 78 segments of Steps. Street Centerlines that are coded as Paper Streets in the OWNER field will remain unchanged in the updated dataset unless the area has been developed. In the event the area has been developed, the Street Centerlines will be modified to reflect the true condition of the visible roads. Street Centerlines that are coded as Vacated in the OWNER field will also remain unchanged in the updated dataset. In the event the area coinciding with the Vacated Streets has been developed, the Vacated Street Centerlines will be removed in order to reflect the true condition of the area. Street Centerlines that are coded as Steps in the OWNER field will be updated to reflect the current condition of the area. The Street Centerlines dataset consists of an external table that links to the supplied coverages and the Geodatabase created for this project using the "-ID" (UserID) field. In order to maintain the link to the external table and not loose valuable data the decision was made to keep all database information currently in the Street Centerline dataset. When a Street Centerline is modified during the update process, the field "UPDATE_YEAR" is set to 2004. All other database attributes will remain unchanged from the original values. All Street Centerline database data with an "UPDATE_YEAR" of 2004 should be verified before used. In some occasions the Street Centerline was divided into two (2) sections to allow for a new road intersection. Both sections of the resulting Street Centerline will have the same database attributes including Address Range. All new Street Centerlines will have zero (0) for "SystemID" and "UserID".

This dataset was previously harvested from Allegheny County’s GIS data portal. The new authoritative source for this data is now the PASDA page (https://www.pasda.psu.edu/uci/DataSummary.aspx?dataset=1224), which includes links to historical versions of the shapefile representations of this data.