Thinking About Working with Geospatial Data? Start Here.

Geospatial data offers a point of view that non-spatial data can’t. With it, retailers can optimize site location, plan promotional outreach, and maximize marketing return based on insight into other area stores, local consumer shopping habits, driving distances, and more. Retailers can then assess competitive impact on a site’s revenue potential, establish cross-sell opportunities, hyper-localize merchandise, or create personalized promotions.

Similarly, insurance companies can use geospatial data for enhanced risk assessment by accessing building location, size, and surrounding characteristics for more accurate underwriting practices. Healthcare providers can leverage this information to assess local health needs and establish the appropriate types of care practices.

Geospatial data can help improve decisions by adding “where” (and sometimes “when”) to “what.” But leveraging it with the other data in your current systems isn’t easy.

The challenge with using geospatial data isn’t that it’s difficult to analyze; the challenge is integrating it into your current systems with the data you already process. It’s important to be aware of these challenges and know how to overcome them.

Preparing your data for analysis is already time consuming and demotivating. Add in complex and very large sets of geospatial data, and both the risk of human error and the time it takes to transform it increase greatly. Such problems are caused mainly by inaccuracy and a lack of standardization.

Geospatial data has a reputation for being inaccurate. The most common issues occur during the data collection process, and can happen in a number of ways: misrepresentation of coordinate systems, incorrect use of units of measure or longitude/latitude, or topological errors (problems with spatial relationships between points, lines, and polygons). When one of these factors is incorrect, any downstream analysis will be affected.

Furthermore, it’s especially difficult to standardize geospatial data because of the vast amount of ways it can be measured or represented (units of measure, timestamps, coordinates, address formats, and more). This lack of standardization makes things difficult, since the data will always be represented in numerous formats.

Manually converting data so everything is represented both accurately and in a uniform manner can be overwhelming and time consuming. But automatically identifying differences between data submissions and enforcing standard rules can reduce manual efforts and accelerate data processing.

This becomes possible with a platform that can integrate with different external sources, process most types of geospatial data formats, and simplify and automate the process of combining, cleaning, and validating the data.

Geospatial data is visualized in analog or digital formats, while tabular data is stored in a table and arranged in rows and columns. Because of this, spatial data has always been studied separately from data science and engineering. This means few data scientists (around 5%) have experience working with geospatial data. This skills gap is why many organizations are not able to effectively incorporate this valuable information into their business.

With a low-code, no-code analytics platform which can process location data, teams can build geospatial workflows without needing to know or understand complex scripting languages. Such tools also let you save the workflows you build in a library so you can reuse and share them with colleagues to upskill other teams in your company. Further, a platform with an environment that enables your team to learn from and use existing geospatial workflows to build upon accelerates the adoption of these practices. A rich community with the willingness to collaborate is key.

Geospatial data is usually analyzed amid a large number of data sets and in complex units of measure, meaning basic BI or analytics tools won’t cut it. They don’t have the processing power or storage capacity to handle how it’s represented.

Think about how large a file size can be for census, cellphone, social media, and weather pattern data. A platform with extensive processing power can easily handle these data sets no matter the size, and can even integrate with the data already in your systems.

The first step is removing these common blockers with the right analytics platform so you can incorporate geospatial analysis into your business. Once you do, it opens up a world of possibilities for better decision-making. For example:

• Improved market/consumer segmentation strategies with a detailed understanding of local demographics and market and trade area data.

• Optimizing site location by accurate assessment of local needs, competitive presence, and revenue potential.

• More accurate risk assessment based on location, surrounding attributes, weather patterns, etc.

• Enhanced proximity analysis with accurate location data and distance between geographic points.

Overall, geospatial analytics can help with trade area analysis, supply chain planning, environment management, fraud detection, and much more. It is even useful in understanding complex problems with regional components like climate change, public health issues, and political conflicts, as well as insights into competitive intelligence and consumer insights.

Low-code/no-code, open source analytics platforms are changing the way we incorporate geospatial analysis into business. They provide a user-friendly interface for building workflows to analyze data and apply results (with or without code), enabling non-experts to work with geospatial data more easily. They may also offer pre-built components for common geospatial tasks such as mapping, geocoding, and spatial analysis, as well as integrations with useful geospatial data sources.

What’s more, their open source nature enables the spread of geospatial knowledge through libraries of saved workflows and spaces to collaborate (both inside and outside company walls). This means organizations with limited resources or little expertise can still benefit from the power of geospatial analytics.

For example, Harvard University’s Centre for Geographic Analysis uses KNIME specifically to leverage workflows created by experts and adapt them to their own needs. This helps students and other analysts understand which ones are useful to the field of study, and how to implement them step by step. This practice not only builds geospatial data knowledge, but also awareness of how to use and integrate it effectively.

The right data analytics platform can integrate with, combine, and transform different kinds of geospatial data, process large amounts and complex types, and help business leaders understand what geospatial intelligence can achieve for their business.

Learn more about KNIME’s geospatial analytics capabilities here:

• Geospatial Analytics Extension for KNIME

• Geospatial Analytics Examples on the KNIME Hub