Places - United States of America (Building Shapes)

A robust dataset featuring detailed attributes of building locations across the USA, including brand affiliations, business categories, and precise geolocations. This comprehensive collection aids in real estate assessment, urban planning, and commercial analysis, helping users visualize and strategize with precision.

Insightful Key Metrics at a Glance for U.S.A Building Shapes

Get acquainted with the breadth and depth of our dataset through these essential statistics.

12885

Total Number of records
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7

Tier 1 Categories
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186

Tier 2 Categories
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7410

Have website
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Get acquainted with the breadth and depth of our dataset through these essential statistics.

Key Variables

Get acquainted with the breadth and depth of our dataset through these essential statistics.

Name
Description
Type
phone_number
String
Contact phone number of the POI.
geom
GeoJSON
Geospatial geometry data in GeoJSON format.
website
String
URL of the POI's website.
opening_hours
JSON
Operating hours in a JSON format.
stock_ticker
String
Stock market ticker symbol for publicly traded entities.
parent_organization
String
The main company or entity that owns the POI.
geo_h3_id_level_8
String
H3 geocode for spatial indexing at level 8 precision.
tier5_naics_category
String
Description of the quinary NAICS category.
longitude
Float
Geographic longitude of the POI.
street_address
String
Physical address of the POI.
latitude
Float
Geographic latitude of the POI.
country_code
String
ISO code for the country of the POI.
business_status
String
Current operating status of the business (e.g., active, closed).
tier5_naics_code
Integer
Most specific NAICS code, under tier4.
tier4_naics_category
String
Description of the quaternary NAICS category.
tier4_naics_code
Integer
Even more specific NAICS code under tier3.
tier3_naics_category
String
Description of the tertiary NAICS category.
tier2_naics_category
String
Description of the secondary NAICS category.
tier2_naics_code
Integer
More specific NAICS code under tier1.
tier3_naics_code
Integer
Further refined NAICS code under tier2.
brand
String
Brand associated with the POI, if applicable.
tier1_category
String
Primary industry or activity category.
tier1_naics_code
Integer
Broad NAICS code representing the primary industry.
poi_name
String
The name of the point of interest.
tier1_naics_category
String
Description of the primary NAICS category.
tier2_category
String
More specific sub-category under tier1.

Use Cases

How can this dataset benefit you?

Enhanced Property Development

Access detailed building data to make informed decisions about property development, renovations, and real estate investments.

Optimized Retail Planning

Leverage extensive data on commercial entities to optimize retail outlet placement, understand competitive landscapes, and assess market saturation.

Targeted Marketing Campaigns

Utilize the dataset to identify and categorize potential commercial locations for highly targeted marketing and advertising strategies.

United States of America's Buildings Places Data (Polygon Shapes) - Everything You need to know

From precise geolocations to business categories and brand affiliations, the dataset encapsulates a wide array of details. This enables users to visualize and strategize with an unparalleled level of precision.

“The ability to dive deep into the specific attributes of each building location helps you make informed decisions and craft well-thought-out strategies.”

By leveraging this dataset, you can:

  • Assess the value and potential of real estate investments
  • Plan urban developments with a clear understanding of existing infrastructure
  • Analyze commercial zones for better business positioning

The possibilities are as varied as they are powerful, opening up new avenues for innovation and insight.

Poi_name

The name of the point of interest (POI) typically represents businesses, retail locations, or notable landmarks within a specific geographic area. These names are crucial identifiers for mapping and geospatial analysis, providing a clear reference to particular sites.

Brand

When analyzing a building location, knowing the associated brand can enhance your understanding of the property's market position and competitive landscape. This data point helps in identifying the presence of well-known chains and franchises, which can significantly impact the value and attractiveness of nearby properties. By including brand affiliations, you gain a nuanced perspective of the commercial ecosystem, crucial for strategic decision-making in real estate assessment, urban planning, and commercial analysis.

Tier1_category

Building Tier 1 category Number of Buildings
businesses and services 5697
healthcare 1806
retail 1650
food and dining 1032
public places 700
transportation 558
miscellaneous 459
automotive 422
sports and recreation 331
miscellaneous 157
travel 73

The table provides insights into the distribution of buildings across various Tier 1 categories in the dataset:

  • Businesses and Services: The dominant category with 5,697 buildings, indicating a strong presence of service-oriented enterprises in the area covered by the dataset.
  • Healthcare: Represents 1,806 buildings, suggesting a significant infrastructure dedicated to health services, which could be critical for analyses related to public health and service accessibility.
  • Retail: With 1,650 buildings, this category underscores a robust commercial sector, important for economic analyses and retail market studies.
  • Food and Dining: Comprising 1,032 buildings, highlighting areas potentially rich in dining options that could appeal to marketers and urban planners focusing on lifestyle and consumer behavior.
  • Public Places: Encompasses 700 buildings, reflecting spaces likely dedicated to government, cultural, or community activities, important for urban development and civic planning.
  • Transportation: Includes 558 buildings, pointing to facilities that support movement such as bus stations, airports, and train stations, crucial for transport logistics and infrastructure planning.
  • Miscellaneous: With 459 buildings, this category could include diverse types not classified elsewhere, offering niche opportunities for specific market analyses.
  • Automotive: Accounts for 422 buildings, relevant for automotive sales, service centers, and related industries.
  • Sports and Recreation: With 331 buildings, indicating recreational or sports facilities that could be key for community health and leisure industry studies.
  • Unspecified Category: With 157 buildings, possibly representing under-reported or emerging sectors within the dataset.
  • Travel: The smallest category with 73 buildings, perhaps highlighting hotels, travel agencies, and other travel-related infrastructure.

These insights help understand the landscape of building uses within the dataset, serving as a foundation for targeted business strategies, policy-making, and economic development projects.

Tier2_category

Under the primary category, a more specific tier 2 category would be "Coffee Shops & Cafés" within “Food & Beverage.” This detailed level of categorization is part of the overall 186 categories and helps in pinpointing specific types of businesses more accurately. Such granularity aids in tailored analysis and strategic planning.

business_status

The business_status indicates whether the business is operational, closed, or undergoing other changes. This helps users quickly identify the current state of each property, making it easier to assess its potential for investment, occupancy, or redevelopment.

street_address, country_code, latitude, longitude

Understanding the geographic details of the Point of Interest (POI) is crucial for spatial analysis, mapping, and geographic market segmentation. The precision of the street address, coupled with the latitude and longitude coordinates, enables businesses and planners to pinpoint exact locations, facilitating more accurate and effective strategies.

These details form the foundation for tasks such as:

  • Site Selection: Identify the best locations based on proximity to target demographics, transportation networks, and other amenities.
  • Market Analysis: Analyze geographic trends and patterns to gain insights into customer behavior and market demands.
  • Urban Planning: Assist in the design and development of urban areas by understanding the spatial distribution of buildings and infrastructure.

By leveraging detailed geographic information, you can enhance your decision-making processes, optimize resource allocation, and ultimately achieve better outcomes for your projects and investments.

phone_number, website

Having precise contact information and a valid website URL is crucial for those looking to dig deeper into property specifics. Whether you’re a potential buyer wanting to schedule a viewing or an urban planner coordinating development projects, these details are vital. In our dataset, we include the phone number and online presence for each property, making it incredibly easy for you to get in touch directly or find further information online.

  • Phone Number: Direct lines to contact property owners or managing agents.
  • Website: URLs to access property listings, business profiles, or further details about the building.

With these resources at your fingertips, you can promptly address any inquiries or gather additional data, streamlining your research and decision-making process.

Why xMap?

xMap empowers businesses with unparalleled location intelligence and comprehensive data analysis, guiding them towards market leadership and growth.

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Frequently Asked Questions

Find answers to commonly asked questions about our spatial analyst platform.

Can I integrate this data with other GIS tools?

Yes, our dataset is compatible with major GIS platforms, allowing for seamless integration and analysis.

Can this dataset be merged with other data for an extensive analysis?

Absolutely. It can be integrated with economic, geographic, and additional datasets for a holistic analysis of Aruba’s socio-economic conditions.

Is it possible to integrate this dataset with other demographic and economic data from xMap?

Yes, the Saudi Arabia Administrative Boundaries dataset can be seamlessly integrated with other xMap datasets, allowing for comprehensive and multi-faceted analysis.

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