Today, companies across all industries are faced with service disruptions, new challenges due to remote work, and high demand for home delivery. And traffic conditions can be profoundly impacted by economic and environmental pressures, which can present an additional challenge in industries that rely on service areas, delivery ranges, or data modeling. With the immense “gig” economy powering a variety of services, many companies have recognized the importance of investing in dynamic and scalable resources.
As such, when it comes to application development, data engineering, UX, IoT, and data science practices, the Maven Wave often designs solutions that incorporate geospatial data to address needs such as accurate traffic predictions. Across these practices, we apply several commonly-used disciplines:
- Persistence & Database Management Systems
- Data Analytics
- Big Data Systems
- Geographic Information Systems (GIS)
The first three disciplines are well-documented and commonly understood by most; however, data professionals are not generally aware of GIS as a discipline. As a result, you may benefit from gaining baseline GIS knowledge, so you can better leverage geospatial data to create spatially aware, outcome-oriented solutions. To this end, this post will shine a light on spatial data, geospatial concepts, and their uses over a range of business use cases.
Across Maven Wave’s practice areas, GIS provides a spatial reference framework and a finer-grained toolset for spatial data acquisition, spatial data modeling, spatial data analysis, and geo-visualization. When spatial data are layered as foundational elements of a solution, we are able to provide deep value and insights that may have been missed or not well understood when the data are not described or enriched in this way. Spatial Data Science can then be applied at scale and correlations can be made in ways that are not readily apparent without spatial context.
In practice, Maven Wave has applied our geospatial expertise across a range of business verticals and use cases. One geospatial concept for understanding and describing data in unexpected and immediately impactful ways is Isolines.
What is an Isoline? In order to understand the usefulness of Isolines, it is first necessary to have a quick primer. The prefix ‘iso’ comes from Greek where it has the meaning ‘equal’. Isolines are lines or polylines (polygons) which join point data with the same values.
Contour lines are the most commonly-recognized isoline. These are the lines drawn to join all the points that are the same height above sea level on a map.
Isobars are another type of isoline that anyone who has watched the evening news can readily identify from their local weather forecast – the line that joins regions of the same atmospheric pressure. You’ll find many of the 35 distinct “isoline” categories are meteorological, nautical, geological, or agronomic in nature.
Isotacs join points where ice begins to melt at the same time each spring. Isophenes join biological events, like the range of flowering plants or bulbs emerging, or anthropogenic events such as population density, pollution, or more recently, COVID-19 data points. There are also Geosocial data and behavioral segments, such as product affinity or sentiment data points that can be represented as isolines.
Isolines that do not follow any of the conveniently-existing names are simply called Isopleths. An isopleth describes any isoline that joins points of the same value such as population or dog ownership. (Isopli̱thysmós and Isokynikós do not exactly roll off of the tongue, so we broadly call them isopleths.)
When isolines are enriched with Google Places and/or Local Context API data, there are boundless possibilities for understanding human activity in real-time. As such, there are two types of Isolines that have remarkable value across a range of business use cases: isochrone and isodistance polylines.
Isochrones & Isodistance
An isochrone is a polyline connecting points with the same “ETA” (given traffic) from a center point, while isodistance is a polyline of points with equal driving distance from a center point. These kinds of isolines can be used to identify service areas, delivery range, sales territories, local competition and improve location awareness or accuracy of data modeling. As you can imagine, this concept is incredibly powerful for time and distance predictive analysis.
The most obvious data to initially utilize in conjunction with isochrone and isodistance polylines are the client’s own geospatial data. In retail, these can simply be store locations. In logistics, this may be distribution center locations; in banking, branch locations; in healthcare, hospital locations; and so on. At a finer grain, these geospatial datasets could represent customer or patient location data. Or at an even finer grain, these can be IoT device telemetry data.
In the simplest use cases, these point data are all that is required to gain geospatial insights, such as points within polygon queries. We also look to enrich these datasets with publicly and commercially available geospatial data such as census/demographic data, mobility data, and market data.
Isochrones & Isodistance in Real-Time
One of the most meaningful ways that we can enrich Isochrones with Google APIs is by utilizing the Google’s Distance Matrix (DM) API. This API provides distance and time for a matrix of origin and destination points based on real-time traffic conditions. By orchestrating these requests to adjust the vertices of an isochrone, we can create isochrones (drive time polygons) that change throughout the day as traffic conditions change.
Isolines at Scale
For these solutions to scale, isolines require a tremendous amount of compute and storage to persist, enrich, and orchestrate. Backend, orchestration, and API isoline services are underserved as a public cloud / scaled offering. As a result, Maven Wave is offering Isolines as a Service (ISaaS) for customers looking to create custom isolines in conjunction with (and enriched by) Google data (Google Maps and Google APIs).
Some common use cases for ISaaS include:
- Real Estate – Give your users the best house hunting experience by providing the best POIs within actual drive times of their selected location.
- Freight Logistics – Give your drivers the ability to know the right place to stop within their allotted drive time based on most attractive Google POIs.
- Service Areas – Optimize the number of customers you are reaching based on traffic conditions.
- Covid Risk Analysis – Help determine how likely segments of your workforce are affected by COVID-19 hotspots and who should or should not come back into the office.
- Financial Services – Improve risk analysis by better evaluating your portfolio with ISaaS combined with other data sources.
- Insurance – Provide more accurate quotes using customer data to determine exactly what someone is typically expected to encounter on a daily basis.
With this service offering, our ISaaS platform handles the complexities of data wrangling, feature enrichment, orchestration, persistence, and serving at scale. Additionally, through our Application Development and UX practices, we can further help your teams build rich and highly customized application integrations and geo-visualizations atop these sets of services – all that you need to bring is your vision.
Contact Maven Wave to request a demo and discuss your use case with our team today.
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