7/31/2023 0 Comments Drivetime hoursFor added accessibility, we will also scope the application to run on a single GPU with around 24-32GB of memory. For the sake of simplicity and accessibility of this demo, we will use open-source data. While there are large and highly current datasets available for this type of information, they are generally not publicly available or are very costly. Like all data visualization projects, it will take a few iterations to dial in. However, the idealized workflow above needs to be translated into real world functionality. Append data like demographics and competitor information filtered by the point in polygon operations of the polygon area.Create a bounding polygon from the furthest nodes.Traverse that network using an SSSP (single source shortest path) algorithm and identify all the nodes within some distance.Identify the node in that network that is closest to that point.So how can one calculate a drive-time area? The general flow is as follows: Additionally, these algorithms can be challenging to scale across large geographic areas like states or countries and even harder to interact with in real time.īy combining the accelerated compute power of RAPIDS cuDF, cuGraph, and cuSpatial with the interactivity of a Plotly Dash visualization application, we are able to transform this complicated problem into an application with a simple user interface. However, demographics, competitor, and traffic analytics datasets can be large, diverse, and difficult to crunch – even more so if complex operations like geospatial analytics are involved. How many customers live in that isochrone? What is the average household income? How many of my competitors are in the area? Is that area a “food desert” (limited access to supermarkets, general affordable resources) or a “food oasis”? Is this area highly trafficked? Answers to these questions are incredibly valuable when making a decision about a potentially multi-million dollar investment. Once a retailer has calculated the isochrone, they can combine it with other data like demographics or competitor datasets to generate insights about that serviceable area. If one uses an “as the crow flies” methodology and specifies a 5 mile radius, one might be including too many customers in an urban area or excluding customers in a less dense, rural area. Isochrones are also more robust to the differences between urban, suburban and rural areas. Instead, it might be easier to hop on the highway and drive 5 miles but instead arrive in 5 minutes. A location might be 2 miles from a customer, but due to dense traffic, it might take me 10 minutes to get there. a circle)? In Retail, time is one of the most important factors when going to a store or shipping out a delivery. Why are isochrones sometimes used instead of “as the crow flies” (i.e.
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