# Urban Attractors: Discovering Patterns in Regions of Attraction in Cities

## TL;DR

This paper presents a framework for classifying urban districts by their attractiveness to visitors using mobile data, identifying three main attraction types in Riyadh, and linking POI types to these patterns for urban planning.

## Contribution

It introduces a novel classification framework for urban attractors based on trip data and statistically relates POI types to attraction patterns, aiding urban development policies.

## Key findings

- Identified three types of urban attractors: Global, Downtown, Residential.
- Demonstrated the relationship between POI types and attraction patterns.
- Provided a method for detecting attraction patterns using mobile data.

## Abstract

Understanding the dynamics by which urban areas attract visitors is significant for urban development in cities. In addition, identifying services that relate to highly attractive districts is useful to make policies regarding the placement of such places. Thus, we present a framework for classifying districts in cities by their attractiveness to visitors, and relating Points of Interests (POIs) types to districts' attraction patterns. We used Origin-Destination matrices (ODs) mined from cell phone data that capture the flow of trips between each pair of places in Riyadh, Saudi Arabia. We define the attraction profile for a place based on three main statistical features: The amount of visitors a place received, the distribution of distance traveled by visitors on the road network, and the spatial spread of where visitors come from. We use a hierarchical clustering algorithm to classify all places in the city by their features of attraction. We detect three types of Urban Attractors in Riyadh during the morning period: Global which are significant places in the city, Downtown which the central business district and Residential attractors. In addition, we uncover what makes these places different in terms of attraction patterns. We used a statistical significance testing approach to rigorously quantify the relationship between Points of Interests (POIs) types (services) and the 3 patterns of Urban Attractors we detected. The proposed framework can be used for detecting the attraction patterns given by type of services related to each pattern. This is a critical piece of information to inform trip distribution models.