QUADRANT DATA OVERVIEW

Product Overview

Data Dictionary

Global Data Counts

About Mobile Location Data

 

RESOURCES

Cross Account Bucket Access

A. AWS S3

 

Getting Started with Quadrant Mobile Location Data

A. Create a Database, Table and Partition

B. How To Run Basic Location Data Queries

i. Scale and Trend

ii. Depth

iii. Accuracy

C. How Geohash Works (Coming Soon)

 

All You Need To Know About Data Evaluation

A. Best Practices

B. SDK vs Bidstream Data

C. Data Evaluation using AWS Athena

 

Location Data Algorithms and Queries

A. Geo-fencing Query

B. Footfall Query

C. Nearest POI Model

D. Location Algorithms

 

SDK INTEGRATIONS

Android Integration

iOS Integration

Integrate with Unity3D for Android

Integrate with Unity3D for iOS

 
ASIA PACIFIC DATA ALLIANCE

About The Alliance

The Data

Use Cases & Data Science Algorithms

Access APAC Data Alliance Data with AWS S3

 
MEDIA LIBRARY
FREQUENTLY ASKED QUESTIONS

 

 

 

 

USE CASES AND DATA SCIENCE ALGORITHMS


Quadrant has built pre-configured algorithms and models for quick and efficient analyses of location data for the COVID-19 pandemic.

Key Features

  - Ease of Use

    These algorithms and models need minimal configuring. Users can set up and run these models within minutes.

  - Actionable Information

    The output derived from these algorithms and models will provide immediate actionable information.

  - Up to Date Algorithms and Models Up To Date

    These algorithms and models were developed to solve real world needs and will be updated to reflect the latest industry needs.

 

Use Cases

The following queries and algorithms, together with Quadrant’s location data, can be used to:

  - Identify people who live in areas/zones/clusters that are affected by the COVID-19 virus

  - Marrying medical data (such as chest X-ray results) with location data, may be able to uncover hotspots where people contract the virus more seriously (see link to article)

  - Identify people who visited at specific time-frames known COVID-19 clusters

  - Identify people who have been in contact with people who had previously visited high risk areas or known clusters

  - Identify people who have been in contact with people who had previously visited other countries which are particularly high risk (e.g. Italy, China, USA, Iran and more).

  - Derive quick statistics and numbers of people living in and visiting high risk areas or known clusters on an on-going basis.

  - Based on known high risk areas or clusters, identify other potential high-risk areas that people frequently visit (cluster tracing)

 

 

Geo-fencing Query

Geo-fencing Query

  - Description: Create customized geofences around high risk zones or known COVID-19 clusters to obtain the device IDs of distinct users that fall within the specified area, that can vary in scale and shape – as small as a store or as big as a city.

  - Output: List of device IDs of distinct users that fall within the specified area.

 

 

Clustering Algorithm

Clustering Algorithm

  - Description: Identify areas or clusters of locations where people who are tested positively or highly exposed to the virus spend the most time

  - Output: Centroid of clusters for given input coordinate data

 

 

Quadrant Circle or Polygon Geofence Footfall Query

Quadrant Circle or Polygon Geofence Footfall Quert

 

  - Description: Calculate footfall within areas or clusters which have been highly affected, or that are at risk.

  - Output: Total number of distinct devices which are seen within an area.