Geospatial Data: Ultimate Guide with Geospatial Databases

What is Geospatial Data?

Geospatial data is geo-referenced information that can be used to identify and locate physical objects anywhere on Earth. It is collected from various spatial data sources such as remote sensing, aerial photography, geocoding, and surveys. Geospatial data has become increasingly important in many fields. It provides a comprehensive overview of an area or region - which means it’s useful for a whole range of businesses and organizations who need accurate intelligence about the real world. In general, the main use cases and applications for geospatial data includes mapping and analysis for urban planning, environmental monitoring, disaster management, navigation systems development, natural resource management.

Geospatial products are becoming more accessible than ever before through open-source software tools and cloud-based services. As a result of increased data access, the importance of on-demand, trustworthy and accurate geospatial intelligence will become a must for many organizations in the years ahead.

What are examples of Geospatial Data?

Here’s some examples of what geospatial data looks like for different users in terms of data models and attributes:

Topographic Maps: Geospatial data is the parent category of map data. As such, there’s some overlap between the two categories. Much like map data, geospatial data helps create powerful visual models relating to the physical world. One such model is a topographic map. These are maps that show location information the elevation, geology, and other physical features of an area. Topographic maps are commonly used by geographers, geologists, surveyors and engineers to gain a better understanding of the landscape. In fact, geospatial data very often comes in map format - it’s the most basic way of representing physical landscapes.

Satellite Imagery: This type of data is collected from aerial or space-based sources which can be used to map out terrain, vegetation, land use patterns, and more. Satellite imagery can be used to map out a large area to a high degree of detail. That’s why it’s been used for GIS observation for several decades now.

GIS Data: Geographic Information System (GIS) data consists of georeferenced information such as points (x-y coordinates), lines (paths) and polygons (areas). It also includes geospatial layers such as street networks, building footprints, administrative boundaries and land cover information.

Digital Elevation Models: A digital elevation model (DEM) is a 3D representation of a surface based on sampled elevation points. DEMS consist of regularly spaced grids along with geographic locations in order to locate the highest and lowest points in an area accurately at any given time frame or scale.

Digital Terrain Models: Digital Terrain Models are derived from DEMs by creating elevated surfaces between grid sample points using multiple methods. These methods include contour lines interpolation or triangulation models such as TIN/DTED file formats commonly used for geospatial analysis applications like watershed delineation.

Vectors and attributes: Vectors in geospatial data are points, lines, and shapes. Attributes are descriptions about the geospatial data like postal code, lat/long coordinates, and terrain type.

What is Geospatial Data used for?

Now that we have explored some examples, let’s see how companies use geospatial data. Basically, geospatial data is used to show where things are on a map. It tells us about the location and shape of different things, like buildings, cities, countries, and landscape features like rivers or landmarks. These are the main geospatial data use cases:

Location-based Services: Geospatial data is used to provide location-based services (LBS) such as geotagging, geofencing, and geocoding for applications like navigation, mapping services, and location-based BI.

Logistics & Transportation Planning: Companies use geospatial data to optimize their logistics plans and fleet management with the help of route optimization softwares, moving vehicle databases and map APIs that give real time traffic updates which helps plan better routes.

Agriculture: Geospatial analytics enable farmers to make informed decisions by leveraging precise weather conditions or soil analysis with geolocation data on a specific field or plantation area.

Disaster Management & Emergency Response: Geospatial information helps in disaster management activities by providing organizations with critical insights into areas prone to natural disasters such as floods or earthquakes so they can respond quickly during an emergency situation.

Real Estate & Property Development: With geospatial data, developers can evaluate market trends and consumer preferences when planning new real estate projects like condominiums or commercial buildings in order to maximize returns on investments.

Market Research: Companies utilize geospatially enabled consumer segmentation models which help them identify relevant customer segments for targeted marketing campaigns.

Urban Planning: Geospatial data is used in urban planning. It helps planners understand how people use the city and how it can be improved. Geospatial data also helps them identify areas that need better infrastructure, like transportation or housing.

What are Geospatial Data products?

Geospatial data products can be delivered in a number of methods, from on-demand API streams or pre-packaged geospatial databases. Typically, products include geo-referenced information such as coordinates, elevation, land cover, shapes and boundaries, geodetic networks and geodesy. Spatial data products can come in many forms such as raster maps, vector graphics, geocoded imagery or orthoimagery (aerial or satellite images), geo-referenced video and audio recordings, as well as information like field observations taken from mobile devices.

All geospatial products have a geographic reference system that is used to accurately place the information in relation to other geographic features on the earth’s surface. This reference system is usually specified with latitude and longitude coordinates but may also include more specific reference systems like Universal Transverse Mercator (UTM) or State Plane Coordinate System (SPCS).

Examples of Geospatial Databases and Datasets

When it comes to geospatial data, there are certain attributes that are typically present in geospatial products. These include latitude and longitude coordinates, spatial extent (bounding box), geometries (points, lines and polygons) as well as other attributes such as area of interest or administrative boundaries. Other columns can also be included depending on the specific application, such as elevation or population density. Additionally, some datasets may contain additional information such as time-stamps or land cover type. All these elements combined provide a comprehensive view of the geospatial landscape which is essential for analysis and decision-making.

What are typical attributes included in Geospatial Data products?

The actual attributes of geospatial databases can vary depending on the data product, but as we’ve seen, there are some typically attributes you can expect to find in any product you buy from a geospatial data marketplace.

These common GIS data attributes could be:

• Location of buildings, cities, countries, and other relevant points of interest
• The level of congestion or traffic in certain areas.
• Tourism statistics based on location
• Temporal information i.e. the time period covered, the historical lookback of the data
• Insight on the lifestyle of customers
• Sites where renewable resources are found
• The extent of floods and other natural disasters

As this list of attributes shows, most providers focus on the coordinates of the earth, while there are others which also bring the characteristics of an event or object into consideration. A lot of other data providers also focus on the time and context when processing attribute information. A lot of data providers also release weekly reports covering the fluctuations in geospatial data for better and updated insights. This provides more flexibility than a static geospatial database.

To understand exact what information an attribute relays, it’s worth asking geospatial data providers for a data dictionary so you can better understand what their product offers. The data dictionary will look something like this:

Geospatial Data Formats and Data Types

Geospatial data is typically stored in geodatabases which are collections of geospatial datasets that use a variety of formats such as vector, raster and/or tabular. Let’s take a closer look into those formats:

Vector GeoDatabases
Vector data shows points, lines and shapes on the earth’s surface. It helps us to find out about buildings, cities, countries and other important places. Vector data can also tell us if an area is busy or if there are any natural disasters in a particular region.

Vector data is typically stored and transmitted in geospatial vector formats such as ESRI Shapefiles, GeoJSON and KML. These geospatial vector formats are used to represent points, lines, polylines and polygons representing geographic shapes and locations on the earth’s surface.

Vector geospatial data can also include additional information like attributes associated with specific geometries or coordinates. The most common vector formats are Shapefiles (an open format that stores geospatial data as a collection of different files with a common filename prefix), GeoJSON (created specifically for encoding and exchanging geographic information on the web) or Keyhole Markup Language (Known as KML, this an XML-based markup language used to display 3D models of earth’s surface in virtual globe software such as Google Earth or Google Maps)

Raster Data
Raster data is geospatial data that is composed of cell-based graphical elements, each of which contains digital values to represent a certain feature on the Earth’s surface. It is typically used to display aerial photography, scanned images of maps, satellite imagery, or other georeferenced data.

Digital Elevation Models (DEMs)
Digital Elevation Models (DEMs) are geospatial data products that represent the elevations of a particular area, usually in the form of a digital raster grid. DEMs are usually created from aerial LiDAR surveys or from satellite imagery, and can be used to calculate a range of geospatial characteristics such as slope,

Geocodes
Geocodes are numerical identifiers used to identify specific geographic locations. They are typically used in geospatial data products to precisely define a location on the surface of the Earth. Geocodes are usually composed of two elements - an area code and a local code that represent geographies such as states, provinces or countries.

How Geospatial Data is typically priced?

There are some resources like Ordnance Survey which provide open geospatial data. This data is available to the public to democratize access to geographical information, transparency of governments and institutions, as well as social, economic, and environmental opportunities. However, spatial data is largely provided by data-as-a-service (DaaS) companies that are focused on data collection and data ingestion. DaaS companies collect large amounts of open data in order to create rich data products. They transform data into high quality spatial databases - there is always a risk of bad data quality associated with free geospatial data. DaaS companies later sell this data, providing spatial support to companies across the globe.

Most geospatial products from DaaS companies follow one of three pricing models:

• Monthly subscriptions/licensing either flat or based on the number of times data is extracted
• Tiered pricing based on the kind of data extracted
• Custom pricing based on your specific set of requirements

Geospatial Data Collection Process and Quality

Geospatial data can be collected through a variety of methods, including aerial photography, satellite imagery, geophysical surveys, and geographic information systems (GIS). This data is then stored in a geospatial database to allow for easy retrieval and manipulation.

Thankfully, collecting geospatial data is now not as tough as it used to be. Companies can now collect related location data from various sources. Primary geospatial datasets include light detecting and ranging like LIDAR, remote sensing data like RADAR and so on. For instance, qualitative land-use maps are generated based on the high-resolution images received from the satellite.

Geospatial data can also be used to generate 3D models of the land surface or terrain as well as geologic features such as faults, mineral deposits, and aquifers. GIS technology is usually the main tool used for managing geospatial data and developing geospatial applications. Such applications include land use planning, disaster management, navigation systems, climate change studies, natural resource management, surveillance analysis, urban development planning and much more.

How to assess the quality of Geospatial Data?

The quality of data depends on how accurate and precise it is. It’s crucial to analyze the quality of any geospatial data product by getting a data sample or demo before purchasing.

For starters, the reputation of the organization from which you have sourced the data could give you a quick indication of the geospatial data quality. Good quality geospatial data providers will have reviews on data marketplaces like Datarade’s. Other organizations, like government bodies, are also typically trustworthy because they must comply with strict quality assurances.

Here’s a list of things you should check in order to assess the quality of any geospatial dataset and verify samples before purchasing:

Accuracy: Check the accuracy of geospatial data by comparing it to known reference points or other geospatial datasets

Completeness: Verify that all the necessary geospatial data is present in the dataset and that it covers areas of interest accurately

Consistency: Ensure that geocodes are consistent with each other and any related attributes, such as boundaries or labels

Timeliness: Analyze geospatial datasets for currency; sometimes older data can be inaccurate when compared to recent surveying techniques or sources of geographic information

Resolution/Scale: Establish whether a geospatial dataset is at an appropriate resolution level for its intended use

Topology/Network Quality: Validate topological consistency within geodatasets where features share common boundaries and nodes have correct connectivity

Attribution/Metadata Quality: Evaluate metadata to ensure that geographies are correctly attributed (such as property lines)

Format Compliance: Confirm that files meet relevant standards and protocols

Semantic Integrity: Check that source material has been interpreted correctly

What are the common challenges when buying Geospatial and Location Data?

Buying geospatial data is a key step in the data collection process. There are a lot of geospatial data aggregators, both government and private which collect geospatial data. It becomes tough to choose one between them, especially when the quality of the data cannot be verified easily.

Data provider comparison is one of the leading issues in the world of external data. The issue is commonly related to lack of standardization within the market. Each geospatial data vendor has its own claims and plus points which makes it difficult to compare between the two provides and reach a conclusion.

Recently, the Open Geospatial Consortium (OGC) has taken steps in order to standardise the geospatial schema specification. Once the OGC standards are adopted by the geospatial community on a widespread basis, the process of finding standardised data sources will be much easier.

What is Spatial Data Management?

Geospatial data management is the process of collecting, storing, organizing, analyzing and managing location information. GIS technology is usually the main tool used for geospatial data management in order to develop geospatial applications, like databases.

A good database management system is essential especially when it comes to analysing large geospatial datasets. Database management allows geospatial analysts to store and organize geospatial structured data so it can be easily accessed and reused.

Where can I buy Geospatial Data?

Data providers and vendors listed on Datarade sell Geospatial Data products and samples. Popular Geospatial Data products and datasets available on our platform are Global Point-of-Interest Data | POI, Geospatial, Sentiment (Reviews), Footfall, Business Listings & Store Location | 200 Million+ POIs Mapped by The Data Appeal Company, Geospatial Data: Places Data | USA, UK, CA | Location Data on 11M+ Places by SafeGraph, and Veraset- Geospatial ‘Visits’ or Point of Interest | US Only by Veraset.

How can I get Geospatial Data?

You can get Geospatial Data via a range of delivery methods - the right one for you depends on your use case. For example, historical Geospatial Data is usually available to download in bulk and delivered using an S3 bucket. On the other hand, if your use case is time-critical, you can buy real-time Geospatial Data APIs, feeds and streams to download the most up-to-date intelligence.

What are similar data types to Geospatial Data?

Geospatial Data is similar to Environmental Data, B2B Data, Energy Data, Real Estate Data, and Commerce Data. These data categories are commonly used for Location Intelligence and Location Analytics.

What are the most common use cases for Geospatial Data?

The top use cases for Geospatial Data are Location Intelligence, Location Analytics, and Analytics.