Evaluating the Effect of the Financial Status to the Mobility Customs

• URL: http://arxiv.org/abs/2106.07909v1
• Date: Tue, 15 Jun 2021 06:47:05 GMT
• Title: Evaluating the Effect of the Financial Status to the Mobility Customs
• Authors: Gerg\H{o} Pint\'er and Imre Felde
• Abstract summary: We determine mobility indicators from one months of Call Detail Records (CDR) data. The results of the PCA investigation showed remarkable correlation of housing prices and mobility customs.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In this article, we explore the relationship between cellular phone data and housing prices in Budapest, Hungary. We determine mobility indicators from one months of Call Detail Records (CDR) data, while the property price data are used to characterize the socioeconomic status at the Capital of Hungary. First, we validated the proposed methodology by comparing the Home and Work locations estimation and the commuting patterns derived from the cellular network dataset with reports of the national mini census. We investigated the statistical relationships between mobile phone indicators, such as Radius of Gyration, the distance between Home and Work locations or the Entropy of visited cells, and measures of economic status based on housing prices. Our findings show that the mobility correlates significantly with the socioeconomic status. We performed Principal Component Analysis (PCA) on combined vectors of mobility indicators in order to characterize the dependence of mobility habits on socioeconomic status. The results of the PCA investigation showed remarkable correlation of housing prices and mobility customs.

Related papers

• Comparative Analysis of Human Mobility Patterns: Utilizing Taxi and Mobile (SafeGraph) Data to Investigate Neighborhood-Scale Mobility in New York City [0.0]
  This study investigates differences in human mobility using taxi trip records and the SafeGraph dataset in New York City neighborhoods. Our findings show that taxi data tends to capture human mobility to and from locations such as Lower Manhattan, where taxi demand is consistently high. In contrast, SafeGraph data excels in capturing trips to and from areas where commuting by driving one's own car is common, but underestimates trips in pedestrian-heavy areas.
  arXiv  Detail & Related papers  (2024-10-21T19:40:23Z)
• Socioeconomic disparities in mobility behavior during the COVID-19 pandemic in developing countries [3.3100431501076844]
  Mobile phone data have played a key role in quantifying human mobility during the COVID-19 pandemic. We leveraged geolocation data from mobile-phone users and population census for 6 middle-income countries across 3 continents. Users living in low-wealth neighborhoods were less likely to respond by self-isolating, relocating to rural areas, or refraining from commuting to work.
  arXiv  Detail & Related papers  (2023-05-11T15:27:00Z)
• Improving (Dis)agreement Detection with Inductive Social Relation Information From Comment-Reply Interactions [49.305189190372765]
  Social relation information can play an assistant role in the (dis)agreement task besides textual information. We propose a novel method to extract such relation information from (dis)agreement data into an inductive social relation graph. We find social relations can boost the performance of the (dis)agreement detection model, especially for the long-token comment-reply pairs.
  arXiv  Detail & Related papers  (2023-02-08T09:09:47Z)
• On Inferring User Socioeconomic Status with Mobility Records [61.0966646857356]
  We propose a socioeconomic-aware deep model called DeepSEI. The DeepSEI model incorporates two networks called deep network and recurrent network. We conduct extensive experiments on real mobility records data, POI data and house prices data.
  arXiv  Detail & Related papers  (2022-11-15T15:07:45Z)
• Exploring the spatiotemporal heterogeneity in the relationship between human mobility and COVID-19 prevalence using dynamic time warping [0.0]
  Previous studies have revealed the correlation between human mobility and COVID-19 cases. This study aims to identify heterogeneities in the relationship between human mobility and COVID-19 cases in U.S. counties.
  arXiv  Detail & Related papers  (2021-09-28T14:38:29Z)
• Characterizing Residential Load Patterns by Household Demographic and Socioeconomic Factors [3.3880352469193475]
  This paper aims to characterize and estimate users' load patterns based on their demographic and socioeconomic information. We develop a deep neural network (DNN) to analyze the relationship between users' load patterns and their demographic and socioeconomic features.
  arXiv  Detail & Related papers  (2021-06-04T15:01:35Z)
• Leveraging Mobile Phone Data for Migration Flows [5.0161988361764775]
  Statistics on migration flows are often derived from census data, which suffer from intrinsic limitations. Alternative data sources, such as surveys and field observations, also suffer from reliability, costs, and scale limitations. The ubiquity of mobile phones enables an accurate and efficient collection of up-to-date data related to migration.
  arXiv  Detail & Related papers  (2021-05-31T13:41:47Z)
• Urban Sensing based on Mobile Phone Data: Approaches, Applications and Challenges [67.71975391801257]
  Much concern in mobile data analysis is related to human beings and their behaviours. This work aims to review the methods and techniques that have been implemented to discover knowledge from mobile phone data.
  arXiv  Detail & Related papers  (2020-08-29T15:14:03Z)
• Magnify Your Population: Statistical Downscaling to Augment the Spatial Resolution of Socioeconomic Census Data [48.7576911714538]
  We present a new statistical downscaling approach to derive fine-scale estimates of key socioeconomic attributes. For each selected socioeconomic variable, a Random Forest model is trained on the source Census units and then used to generate fine-scale gridded predictions. As a case study, we apply this method to Census data in the United States, downscaling the selected socioeconomic variables available at the block group level, to a grid of 300 spatial resolution.
  arXiv  Detail & Related papers  (2020-06-23T16:52:18Z)
• On Disentangled Representations Learned From Correlated Data [59.41587388303554]
  We bridge the gap to real-world scenarios by analyzing the behavior of the most prominent disentanglement approaches on correlated data. We show that systematically induced correlations in the dataset are being learned and reflected in the latent representations. We also demonstrate how to resolve these latent correlations, either using weak supervision during training or by post-hoc correcting a pre-trained model with a small number of labels.
  arXiv  Detail & Related papers  (2020-06-14T12:47:34Z)
• Entropy as a measure of attractiveness and socioeconomic complexity in Rio de Janeiro metropolitan area [52.77024349608834]
  We use a mobile phone dataset and an entropy-based metric to measure the attractiveness of a location. The results show that the attractiveness of a given location measured by entropy is an important descriptor of the socioeconomic status of the location.
  arXiv  Detail & Related papers  (2020-03-23T15:58:56Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.