Why Do We Need a New Product for Geospatial Analytics for Mobility
Our lives are now filled with products and services available at the tap of a phone, getting things delivered in minutes to our doorstep! Today, it is hard to find an app which doesn’t ask for your location permissions.
Which brings me to my next point:
Firstly, there is no “mobility” without dynamic location. In other words, location is a fundamental aspect whenever there is movement of assets (people, vehicles, cargo, parcel) on the ground. After all, in cities and towns where things change at every sq km in a city, it’s important to add the “context” of those areas.
Location is not only about a point on a map. It is about a line. It is about movement.
You might be wondering: “But, why?”
Before we deep-dive into that, let’s understand how geospatial data is different from statistical data.
Location is exciting! If played right, it can drive significant revenue improvements (case in point: Uber & Airbnb). Its high usage and real-time nature make it really valuable and sticky.
Just like text, sound, image are different kinds of data, Latitude-Longitude is a different kind of data that can add immense depth, meaning and insights to statistical data in a space-time context.
It incorporates the third dimension on top: values across time and dynamic location — which requires a completely different approach and treatment. Computing metrics for static assets involves plotting points on a map and calculating metrics for that. Movement analytics concerns itself with how do we visualize, analyze and optimize how things move on the ground!
Two special properties of geospatial data are autocorrelation and non-stationarity, which make it difficult to meet the assumptions and requirements of traditional (nonspatial) statistical methods, like OLS regression.
Let’s answer our pending question now:
Today, the current way of dealing with location data inside companies is broken. Due to the dearth of any location analytics products out there, most of the businesses have no choice but to rely on traditional BI and analytics tools. It’s not puzzling that this is a highly ineffective strategy because these tools are not really meant for geo-analysis.
Moreover, location data is present across disparate databases, in different structures. Hence, slicing and dicing variables across tables in real-time comes even more complex.
All of us know that a statistical dashboard contains bars and charts which sprout from carrying out operations (sum, count, divide, average, etc) on variables. While getting live trend updates through spikes and dips on graphs might be helpful, these charts are work better on aggregated historical data.
Adding or dividing lat-longs and creating bars and charts on them is pretty futile. To make sense of these lat-longs, you need to have a map by your side to understand their spatial distribution!
Maps are also more insightful to draw inferences than bars and charts if there is movement of components on the ground involved. Hence, real-time geographic analysis when everything is dynamic becomes fundamental.
Location intelligence is so much more than tracking and plotting points on a map!
Strategies like clustering, heat mapping, aggregation, indexing, etc. come in handy to absorb a large number of points.
4. Data Preparation
Enriching: Enriching of any data implies adding new layers of information and merging it with third-party or external data sources. In the GIS world, we enrich spatial data for a better context of areas in which the points are present. This means adding the environmental layer of roads, traffic, weather, points of interest, demographics, buildings, etc.
However, the issue doesn’t get resolved here because geospatial data itself comes with a bucket full of challenges.
Performing geospatial queries on streaming data become very compute-intensive and legacy technologies (like ArcGIS) provide very little support. The complexity increases with visualizing large geospatial datasets with any sort of interactivity at scale.
Sometimes developers also build their own internal tools, but most of the times they are not well suited for all different audiences inside the company. Since the tools are not built in a scalable way, maintaining these suck up a lot of developer bandwidth often!
It all started with a personal problem. As data scientists working with geospatial data, the existing analytics products were futile in our daily workflows. Hence, we had to build our own tools and libraries for our everyday workflows.
We then realized data scientists around the globe face similar problems when it comes to location data. As a result, businesses are struggling to attain operational efficiency!
So, the next time you want to order medicines in case of an emergency, you won’t hopefully read on the screen, “Delivery guys are not available. Please check again later.” Next time the delivery guys won’t have to stand idle in the scorching heat, cold or rains waiting for the orders to come.
They can be incentivized to move to high demand areas and can earn more money. The push notifications that you get won’t be spam — they will be shot to you at the right place and right time.
- Bridging Supply-Demand Gaps in Last-Mile Delivery Companies Geospatially [Link]
- Carto vs Kepler vs Locale: Which product to use for geospatial analytics? [Link]