What is Geospatial Data & How to Use It
Spatial data (geospatial data) refers to information about a physical object’s location on a map or coordinate system. It makes the localization of individuals, objects or devices possible anywhere in the world. Spatial data is generally used in mapping activities, such as locating high growth areas in real estate or discovering areas of high strategic importance when planning for a new store location.
What is Geospatial Data?
Geospatial data is everywhere. The weather predictions, routes suggested by Google Maps, store locations, geotagged tweets and more – these are nothing but geospatial data. In simple words, geospatial data is any data related to an object which has its position on the earth – a mountain, horse, lake, river, road, building and countless others.
Some common types of geospatial data include:
- Electric vehicle charging stations data
- Location data
- Satellite data
- Map data
- Store location data
- Marine traffic data
- Road traffic data.
The position of geospatial data could be static or short-term like an earthquake event, the location of the road, or people living in poverty, or it could be dynamic like the spread of infectious disease, moving car and so on. This data is usually stored as coordinates with respect to the earth with its topology mapped.
Geospatial data is important for a range of users. For instance, information related to public amenities, roads, water bodies, and localities is used as a reference for a large number of purposes. This data is widely available as open data, whether collected by private organizations or public agencies.
Who uses Geospatial Data and for what use cases?
Geospatial data has proved to be a new and reliable source of information for executives, marketers, data team leads, business intelligence teams, and all other entities that use third-party data to derive useful insights. It helps them in decision making and provides a clear representation of their physical assets and customers.
For this reason, geospatial data is increasingly used by businesses to determine the location of their next store, warehouse, real estate asset and the like. Undoubtedly, the right location of your physical asset plays an important role in determining the success of your initiative and this is where geospatial data empowers the businesses.
It provides quick insight into the population around an area. It could also help you in picking areas with maximum activity to conduct roadshows and other activities.
Geospatial data is also widely used by internet and mobile app companies. They want to know which apps are most commonly used in a particular location and how users behave in those regions. This way, geospatial data can work as an enrichment to their existing app usage data sets, enabling the demographic analysis of their audience based on its location.
What are typical Geospatial Data attributes?
The actual attributes of geospatial APIs can vary depending on the data providers. 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 location of the attributes.
There a many forms of GIS attributes, for example:
- Location of buildings, cities, countries, and other relevant points of interest
- The level of congestion or traffic in a certain area
- Tourism statistics based on location
- Insight on the lifestyle of customers
- Sites where renewable resources are found
- The extent of floods and other natural disasters
A lot of data providers also release weekly reports covering the fluctuations in geospatial data for better and updated insights.
How is Geospatial Data collected?
You want to understand the actual process of the data collection. The entire data collection process could incorporate a range of techniques like field data collection, geographical information science (GIS), data conversion and remote sensing data processing.
Thankfully, collecting geospatial data is now not as tough as it used to be. Companies can now collect related 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. On the other hand, quantitative land-use maps can be generated from medium-resolution satellite images. These maps are generally used for conducting a regional-scale study.
In addition to providing us with qualitative and quantitative maps, geospatial data is also important for environmental studies like natural resource management, deforestation, and global warming.
To make the most out of Geospatial data, it is essential to have a proper geospatial database for easy analysis and mapping.
How to assess the quality of Geospatial Data?
Not all data is gold. The quality of data depends on how accurate and precise it is. This is why it is important to analyze the quality of Geospatial data.
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. There are a lot of websites and organizations that provide good quality Geospatial data. Government organizations are one of them. However, it is essential to verify the data properly.
How can you do that? If it is accounting data, you can check if the numbers add up and make any sense. If it is textual data, you can look for spelling and grammar errors. However, there is no such solution for geospatial data. But you can take the help of tools like ArcGIS Data Reviewer to get insights into errors and data quality.
The most reliable method to verify geospatial data is to do a visual inspection on your own. Use the map data to verify that objects are in the right location. For example, an automobile accident location displayed in lakes can indicate that the data is not reliable. However, this is random and might not be the best way to analyze it
That said, verifying and assessing the quality of geospatial data is a tough job and depends on various parameters like accuracy, completeness, precision, and consistency.
How Geospatial Data is typically priced?
Most geospatial data APIs follow one of three pricing models:
- Monthly subscriptions/licencing 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
However, there are various free GIS sources too, but the authenticity of data obtained from such sources is always questionable.
What are the common challenges when buying Geospatial Data?
When it comes to buying geospatial data, there are several challenges that one needs to encounter. 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. Further, the data provided by each agency varies depending on the format. Thus, it becomes important to gauge if the provided data format will work fine for your requirements.
Add to that, data distribution agreements also play an important role here. In various cases, local licensing agreements are in place which limits access to information.
What to ask Geospatial Data providers?
Before buying any form of geospatial data, there are some questions you might want to have cleared out:
- How accurate and precise is the data provided?
- Can the dataset be integrated with my current business technologies?
- How frequently is the database updated?
- Is there a sample set available for testing purposes?
You might want to ask other questions as well, depending on your use case.