Using spatiotemporal analytics as a technique to analyze data using geographic and time properties at scale

1. Spatio-Temporal
Analytics use cases
Mehdi Charafeddine
May 2021

2. Why Spatio-
temporal data analysis?
Use cases
• Crop Yields Prediction Using Satellite Imagery
• Realtime Demand Sensing dynamics
• Customer Geotagging for better Targeting and Fraud Prevention
• Supply & Distribution Route Optimization with Spatial Analytics
• Transportation & Traffic management
Features
• Find the distance between two points
• Check whether one area (polygon) contains another
• Check whether one line crosses or touches another line or polygon
• Requires geospatial indexing
• Cool visualisations
“Technique to analyse data using geographic and time properties”

3. The need for a Geospatial Grid
Model
• Grid systems are critical to analysing spatial data sets at scale
− Spatial models are needed to enable numerical problems to be
solved in a broad range of scientific applications
− Representation of data and modelled properties can be discretised
to a grid
− Are not subject to changes from land or cadastral registry
Why a Grid System?
How does a Grid work?
What Grid Models?
• A global grid system requires:
A map projection (e.g. Mercator)
A grid overlaid on top of a map
• Data points are bucketed into hexagons and can be written using the
hexagonally bucketed data
• Grid cells can be assigned a value by interpolation of nearby data points or
by assumptions
• The cell size limits the resolution of the model, smaller cells can represent
higher frequencies, but a denser and larger grid add exponentially to the
computing cost
• Both 2D and 3D models can be built
• There are 3 types of grids: triangle, square, and hexagonal

4. Types of grid systems
Hexagons have only one distance
between a hexagon centerpoint and its
neighbors’, compared to two distances
for squares or three distances for
triangles. This property greatly
simplifies performing analysis and
smoothing over gradients.
• A hexagonal grid system is preferred for the following
reasons
− Hexagons minimize the quantization error introduced when
data is mapped to a field
− It provides the best circle approximation
− Can be easily compacted
• Proposed framework: H3 by Uber (Open Source)
Triangle Grid System Square Grid System Hexagonal Grid System

5. Hexagonal Grids indexing
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Hexagons cannot be
perfectly subdivided into
seven hexagons, so the
finer cells are only
approximately contained
within a parent cell.
The ability to adjust the resolution of the grid is key
Overlay an hexagonal
grid on a specific location
Partition the earth
into hexagons
Adjust the resolution of
the grid as required

6. Grid optimization
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Naïve Representation
• 10,633 hexagons at resolution 6
• Computationally heavy
Optimized Representation
• Compact the hexagons within a
specific polygon boundary
• 901 hexagons at resolutions up to 6

7. Demo example
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Census Tract Data
NYC 311 call data
(noise complaints)
NYC Digital
Elevation Model
Data Sources
1. Raw Data
2. With Hex Grid
3. Smoothed Hex Grid 4. Map overlay

8. Geospatial processing
algorithms (Spark libraries)
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Attribute Aggregation
Movement Tracking
Nearby Summarization
Within Summarization
Batch Analytics
Real-time
Analytics
Data Summarization Data Enrichment
Ref Data lookup
Reverse geocoding
Pattern Analysis
Density Calculation
Gap Detection
Similar Locations
Gap Detection
Geometric Operations
Convex Hull
Enveloppe
Intersect
Project
Union
Cluster Analysis
Classification
Clustering
Dimensional Reduction
Geo-weighted
Regression
Ordinary Least Squares
Hot Spot Detection