Urban attractors: Discovering patterns in regions of attraction in cities

Abstract

Understanding the dynamics by which urban areas attract visitors is important in today’s cities that are continuously increasing in population towards higher densities. 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 daily commuters 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 number of visitors a place received, the distribution of distance traveled by visitors on the road network, and the spatial spread of locations from where trips started. We used a hierarchical clustering algorithm to classify all places in the city by their features of attraction. We discovered three main types of Urban Attractors in Riyadh during the morning period: Global, which are significant places in the city, Downtown, which contains the central business district, and Residential attractors. In addition, we uncovered what makes districts possess certain attraction patterns. We used a statistical significance testing approach to quantify the relationship between Points of Interests (POIs) types (services) and the patterns of Urban Attractors detected.

Predicting the impact of urban change in pedestrian and road safety

“…At the same time, many tasks may be automated to some degree by means of urban sensors and big data analysis. In line with other recent advances which take profit of new and extensive data-sets 6,18,29,41 , machine learning 4,5,5,10,43 and their combination with complex networks tools 16,19,28,30,49,61 , our work focuses on the automated assessment and improvement of urban traffic safety, for both pedestrian and vehicles.…”

“…Up to now, due to lack of available (or open) data and tools for automated analysis, these studies have mostly been constrained either to the limited features available from planimetric map data 16,17,23 , or to detailed, but time-consuming, descriptions of particular settings collected by hand 20,21 . However, the rise of new Big Data sources 13,[24][25][26] related to urban environments and transportation has boosted the development of new techniques 10,[26][27][28] and the combination of complementary tools existing in different fields 5,29 , such as complex systems 18,30 .…”

Renewable Energy Utilization and Environmental Pollution Control in the New City Area Mamminasata Metropolitan, Indonesia

“…Thus, the pattern of population mobility from the direction of new urban areas to activity centers which tends to increase over time has an impact on the complexity of the Mamminasata Metropolitan urban transportation system. The pattern of population mobility is closely related to work, shopping, and social relations based on distance and spatial distribution (Alhazzani et al, 2021;Macedo et al, 2022). The facts found in the field indicate that the pattern of origin and destination of people's journeys has an impact on traffic congestion and an increase in the burden of air pollution originating from motor vehicle exhaust gases.…”

Analysis of Spatiotemporal Changes in the Gravitational Structure of Urban Agglomerations in Northern and Southern Xinjiang Based on a Gravitational Model

“…At this point, cities begin to create regional relationships that fully capture their radiation capability [29]. There must be variations in each city's appeal or an asymmetry in the attractiveness across cities as a result of their varying levels of development [30]. As a result, cities are drawn to one another in both directions [31][32][33].…”