There are various methods to analyze map data, but a basic approach involves plotting coordinates to identify density and clusters. When analyzing data, regional analysis is preferred over analyzing individual data points due to the lack of classification for points within a certain area.
In this article, We are presenting Uber's H3 geospatial indexing system, which is one of the various open-source initiatives by the company. The H3 system is designed to help you quickly and efficiently index your data for later querying. Geospatial indexing is crucial for aggregating and querying data at scale. This type of data is generally abundant, difficult to index or search, and can be structurally complex. What is H3?
H3 is a hexagonal spatial index, global, hierarchical, and open source discrete global grid system, originally developed by Uber for “efficiently optimizing ride pricing and dispatch to set dynamic prices and make other decisions on a city-wide level,” it is now used far beyond the ridesharing industry. H3 is available at 16 different resolutions, with the smallest covering an average area of 0.9m², reaching up to and 4.3 million km² at the largest resolution.
But Why H3?
The hexagonal shape of H3’s grid cells provides several key benefits.
Unlike square-based grids, hexagons make it easier to connect nearby regions while minimizing spatial bias, because a hexagon’s six neighbors are equidistant.
Hexagonal cells can also improve shape-fitting, low area, and angle distortion over the face of the globe, all while reducing margin of error.
All neighboring hexagons have the same spatial relationship with the central hexagon, making spatial querying and joining a more straightforward process.
H3 provides 15 finer grid resolutions in addition to the resolution 0 base cells. The finest resolution, resolution 15, has cells with an area of less than 1m²
H3 Grid in Resolution 8 and 9
An H3 Grid of Hotel Locations and Simple Pricing Distribution Analysis
The first step is to import the hotels locations dataset into Supermap iDesktop. Fill the H3 grid with average price of hotels by update the attribute of the objects in the dataset with the spatial relationship with Dataset Attribute Update tools. The spatial relation of the geometry object in the target dataset relative to the objects in the source dataset. You can get the average of the attributes of hotels to update.
By constructing SQL query expressions, you can find out specific data from existed data. Query the data from average hotel price “is not null” and you will get the result below:
You can extrude the result from 2D data to 3D data to show it more clearly the distribution of the hotel prices In Singapore.