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


LOCATION ALGORITHMS 

Geofence Circle Query

The Circle Geofence Query returns distinct Ad-IDs that fall within the specific circular geofence. Ad-ID is the industry standard for identifying advertising assets across all media platforms. Using this query, we can set a circular geofence and obtain the distinct users who are spotted within this circular geofence. This query can be used in multiple scenarios such as targeted advertising where you want to target users who are spotted in the vicinity of a specified Point Of Interest (POI).

  - Description: Geofence/Extract nearby events within a proximity distance
  - Output: Events/Rows of data that are identified within the input circular geofence
  - Code: Written using HQL
  - Deployment: Available to scale on big data

 

Geofence Polygon Query

The Geofence Polygon Query returns distinct Ad-IDs which fall within a given geofence. Ad-ID is the industry standard for identifying advertising assets across all media platforms. Using this query, we can set a polygon geofence and obtain the distinct users who are spotted within this polygon. This query can be used in multiple scenarios, such as targeted advertising where you want to target users who are spotted within the area of a specified polygon geofence.

  - Description: Geofence/Extract nearby events within a polygon.
  - Output: Events/Rows of data that are identified within an input polygon
  - Code: Written using HQL
  - Deployment: Available to scale on big data

 

Nearest POI Basic Model

The Quadrant Nearest POI Basic Model is a machine-learning model built by Quadrant to identify the nearest POI for a given location's coordinates based on latitude and longitude values. This model is a python-based model built using scikit learn library. It was trained using K-Nearest-Neighbours (KNN) and can also be deployed in Apache Spark framework using PySpark library.

  - Description: Predict the nearest POI
  - Geography: Singapore but can be trained with a POI database from other countries
  - Output: Events/Rows with Nearest POI location name and address
  - Code: Written using Python
  - Deployment: Available to scale on big data

 

Circle Geofence Footfall Query

The Circle Geofence Footfall Query returns daily footfall of a specified circle geofence. Using this query, we can set a circular geofence and obtain the daily footfall within this region. This query can be used in multiple scenarios suck as footfall estimation or understanding daily traffic of the mentioned region.

  - Description: Calculate footfall within a proximity distance.
  - Code: Written using HQL
  - Deployment: Available to scale on big data

 

Polygon Geofence Footfall Query

Quadrant Geofence Polygon Query returns distinct Ad-IDs which fall within a given geofence. Ad-ID is the industry standard for identifying advertising assets across all media platforms. Using this query, we can set a polygon geofence and obtain the distinct users who are spotted within this polygon. This query can be used in multiple scenarios, such as targeted advertising where you want to target users who are spotted within the area of a specified polygon geofence.

  - Description: Calculate footfall within a polygon
  - Code: Written using HQL
  - Deployment: Available to scale on big data

 

Nearest POI Advanced Model

The Nearest POI Advanced Model is a machine-learning model built by Quadrant to identify the nearest POI for a given location's coordinates based on latitude and longitude values. This model is a python-based model built using scikit learn library. It was trained using K-Nearest-Neighbours (KNN) and can also be deployed in Apache Spark framework using PySpark library.

  - Description: Predict the nearest POI; returns more results
  - Geography: Singapore but can be trained with a POI database from other countries
  - Output: Events/Rows of Nearest POI location name, address, category, sub-category
  - Code: Written using Python
  - Deployment: Available to scale on big data

 

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