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

 

 

 

 

LOCATION DATA ALGORITHMS AND QUERIES


NEAREST POI MODEL

This machine learning model identifies the nearest Point Of Interest for a given location based on the latitude and longitude values. Analyse and understand the traveling habits of your audience. Identify time and speed taken to travel from one POI to another. Identify routes frequently taken by your audience between two POI.

Quadrant currently offers two types of Nearest POI Model:


Nearest POI Basic Model:  Identifies the address of the nearest Point of Interest for a given coordinate

Nearest POI Advanced Model:  In addition to the address of the nearest Point of Interest, the advanced model provides the location category of the Point of Interest (food & beverage, retail and more)
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