scholarly journals The analysis of residential sorting trends: Measuring disparities in socio-spatial mobility

Urban Studies ◽  
2018 ◽  
Vol 56 (2) ◽  
pp. 288-300 ◽  
Author(s):  
Tal Modai-Snir ◽  
Pnina Plaut
Keyword(s):  
Residential Mobility ◽  
Large Body ◽  
Methodological Framework ◽  
Mobility Patterns ◽  
Spatial Mobility ◽  
Short Term ◽  
Residential Sorting ◽  
Spatial Level ◽  
Socioeconomic Segregation ◽  
Empirical Demonstration

Ethnic and socioeconomic segregation levels vary over time and so do the spatial levels of these segregations. Although a large body of research has focused on how residential mobility patterns produce segregation, little is known about how changing mobility patterns translate into temporal and scale variations in sorting. This article develops a methodological framework designed to explore how changing mobility patterns reflect such trends. It introduces a measure of sorting that reflects the extent of disparities among groups in their socio-spatial mobility. Trends in the direction and the extent of sorting can be exposed by computing sorting measures over consecutive periods. The measure is broken down to capture the relative contributions of residential mobility to sorting at hierarchically nested geographical units, for example cities and their constituent neighbourhoods. An empirical demonstration shows that changes in residential mobility patterns affect the magnitude and spatial level of residential sorting, which vary even over the short term.

scholarly journals Immigrants’ spatial integration dynamics in Tel-Aviv: An analysis of residential mobility and sorting

Urban Studies ◽  
2020 ◽  
pp. 004209802093613
Author(s):  
Tal Modai-Snir ◽  
Pnina O. Plaut
Keyword(s):  
Soviet Union ◽  
Residential Mobility ◽  
Former Soviet Union ◽  
Joint Effect ◽  
Spatial Integration ◽  
Mobility Patterns ◽  
Data Set ◽  
Tel Aviv ◽  
Residential Sorting ◽  
Mobility Behaviour

Residential mobility patterns of immigrant and majority groups are key in understanding immigrants’ spatial integration. This article explores the spatial integration dynamics of immigrants from the Former Soviet Union in Tel-Aviv, Israel, as reflected in changing residential mobility behaviour. Unlike previous research, the article investigates the simultaneous effect of the relocations of both immigrants and majority members, with treatment of ethnic and socioeconomic dimensions of residential sorting considered simultaneously. Using a unique data set that spans the period 1997–2008, the analysis reveals a dynamic interplay of both groups’ mobility patterns. Their joint effect decreased residential sorting across both neighbourhood dimensions over time. Despite the decreasing magnitude, residential sorting processes remained active by the end of the research period, delaying the spatial integration of immigrants.

scholarly journals The Consequences of Divorce and Splitting up for Spatial Mobility in the UK

2013 ◽  
Vol 38 (2) ◽  
Author(s):  
Peteke Feijten ◽  
Maarten Van Ham
Keyword(s):  
Residential Mobility ◽  
Regression Models ◽  
Disruptive Effect ◽  
British Household Panel Survey ◽  
Union Dissolution ◽  
Panel Survey ◽  
Housing Situation ◽  
Spatial Mobility ◽  
Logistic Regression Models ◽  
The Uk

Union dissolution is well known to have a disruptive effect on the housing situation of those involved, and often leads to downward moves on the “housing ladder”. Much less is known about the geographies of residential mobility after union dissolution. There are, however, reasons to expect that those who experienced a union dissolution have a different likelihood of moving over longer distances than those who stay in a union, because of different moving motives. This study contributes to the existing literature by investigating the occurrences of moves, distances moved and the destinations of moves after union dissolution. The paper also contributes to the literature by investigating the effect on mobility not only of divorce, but also of splitting up and repartnering. Using longitudinal data from the British Household Panel Survey (BHPS), and logistic regression models, we found that union dissolution has a significant effect on the occurrence of moves and on moving distances.

scholarly journals International Newcomers and their direct, indirect and cumulative effect on Ethnic Residential Segregation: A Dynamic Assessment

2021 ◽  
Author(s):  
Eduardo Tapia
Keyword(s):  
Direct Effect ◽  
Residential Segregation ◽  
Residential Mobility ◽  
Dynamic Assessment ◽  
Cumulative Effect ◽  
Cumulative Effects ◽  
Mobility Patterns ◽  
Residential Areas ◽  
Ethnic Residential Segregation ◽  
Dynamic Perspective

Previous studies show households' selective residential mobility as a principal cause of residential segregation. However, a less studied aspect of residential segregation has been how foreign newcomers affect those mobility patterns and consequently residential segregation trends. This paper extends previous investigations by evaluating the effects of newly arrived immigrants on ethnic residential segregation from a dynamic perspective. Unlike previous studies, this study analyzes newcomers' neighborhood choices together with their direct, indirect, and cumulative effects on segregation. Results show that immigrant settlements not only exacerbate residential segregation by landing in already segregated areas (direct effect) but by triggering segregating promoting movements in households living in destination neighborhoods (undirect effect). Both results contribute to producing a higher level of segregation compared with a situation where newcomers would have been randomly allocated across the residential areas (cumulative effect). These findings highlight the importance of reception strategies in host cities to palliate segregation levels and demonstrates its cumulative effects.

scholarly journals Identifying Foreign Tourists’ Nationality from Mobility Traces via LSTM Neural Network and Location Embeddings

2019 ◽  
Vol 9 (14) ◽  
pp. 2861 ◽  
Author(s):  
Alessandro Crivellari ◽  
Euro Beinat
Keyword(s):  
Neural Network ◽  
Motion Tracking ◽  
Large Scale ◽  
Short Term Memory ◽  
Human Mobility ◽  
User Profiling ◽  
Personalized Recommendation ◽  
Mobility Patterns ◽  
Trajectory Data ◽  
Short Term

The interest in human mobility analysis has increased with the rapid growth of positioning technology and motion tracking, leading to a variety of studies based on trajectory recordings. Mapping the routes that people commonly perform was revealed to be very useful for location-based service applications, where individual mobility behaviors can potentially disclose meaningful information about each customer and be fruitfully used for personalized recommendation systems. This paper tackles a novel trajectory labeling problem related to the context of user profiling in “smart” tourism, inferring the nationality of individual users on the basis of their motion trajectories. In particular, we use large-scale motion traces of short-term foreign visitors as a way of detecting the nationality of individuals. This task is not trivial, relying on the hypothesis that foreign tourists of different nationalities may not only visit different locations, but also move in a different way between the same locations. The problem is defined as a multinomial classification with a few tens of classes (nationalities) and sparse location-based trajectory data. We hereby propose a machine learning-based methodology, consisting of a long short-term memory (LSTM) neural network trained on vector representations of locations, in order to capture the underlying semantics of user mobility patterns. Experiments conducted on a real-world big dataset demonstrate that our method achieves considerably higher performances than baseline and traditional approaches.

scholarly journals A Geolocation Analytics-Driven Ontology for Short-Term Leases: Inferring Current Sharing Economy Trends

Algorithms ◽  
2020 ◽  
Vol 13 (3) ◽  
pp. 59
Author(s):  
Georgios Alexandridis ◽  
Yorghos Voutos ◽  
Phivos Mylonas ◽  
George Caridakis
Keyword(s):  
Building Blocks ◽  
Sharing Economy ◽  
Urban Landscapes ◽  
Methodological Framework ◽  
Short Term ◽  
Multimodal Data ◽  
Labeled Graph ◽  
Current Sharing ◽  
High Level

Short-term property rentals are perhaps one of the most common traits of present day shared economy. Moreover, they are acknowledged as a major driving force behind changes in urban landscapes, ranging from established metropolises to developing townships, as well as a facilitator of geographical mobility. A geolocation ontology is a high level inference tool, typically represented as a labeled graph, for discovering latent patterns from a plethora of unstructured and multimodal data. In this work, a two-step methodological framework is proposed, where the results of various geolocation analyses, important in their own respect, such as ghost hotel discovery, form intermediate building blocks towards an enriched knowledge graph. The outlined methodology is validated upon data crawled from the Airbnb website and more specifically, on keywords extracted from comments made by users of the said platform. A rather solid case-study, based on the aforementioned type of data regarding Athens, Greece, is addressed in detail, studying the different degrees of expansion & prevalence of the phenomenon among the city’s various neighborhoods.

Urban transformations, migration and residential mobility patterns in African secondary cities

2017 ◽  
Vol 117 (2) ◽  
pp. 93-104 ◽  
Author(s):  
Manja H. Andreasen ◽  
Jytte Agergaard ◽  
Robert B. Kiunsi ◽  
Ally H. Namangaya
Keyword(s):  
Residential Mobility ◽  
Mobility Patterns ◽  
Urban Transformations

Socio-spatial Mobility and Residential Sorting: Evidence from a Large-scale Survey

Urban Studies ◽  
2012 ◽  
Vol 49 (15) ◽  
pp. 3253-3270 ◽  
Author(s):  
William A. V. Clark ◽  
Philip S. Morrison
Keyword(s):  
Large Scale ◽  
Spatial Mobility ◽  
Residential Sorting ◽  
Large Scale Survey

scholarly journals Comparing Twitter and LODES Data for Detecting Commuter Mobility Patterns

2021 ◽  
Author(s):  
Jochen Albrecht ◽  
Andreas Petutschnig ◽  
Laxmi Ramasubramanian ◽  
Bernd Resch ◽  
Aleisha Wright
Keyword(s):  
Exploratory Research ◽  
Mobility Patterns ◽  
Short Term ◽  
Traffic Demand ◽  
Trip Purpose ◽  
Social Network Data ◽  
Twitter Data ◽  
Rapid Changes ◽  
The Common ◽  
Us Census

Local and regional planners struggle to keep up with rapid changes in mobility patterns. This exploratory research is framed with the overarching goal of asking if and how geo-social network data (GSND), in this case, Twitter data, can be used to understand and explain commuting and non-commuting travel patterns. The research project set out to determine whether GSND may be used to augment US Census LODES data beyond commuting trips and whether it may serve as a short-term substitute for commuting trips. It turns out that the reverse is true and the common practice of employing LODES data to extrapolate to overall traffic demand is indeed justified. This means that expensive and rarely comprehensive surveys are now only needed to capture trip purposes. Regardless of trip purpose (e.g., shopping, regular recreational activities, dropping kids at school), the LODES data is an excellent predictor of overall road segment loads.

scholarly journals Measuring the Differentiated Impact of New Low-Income Housing Tax Credit (LIHTC) Projects on Households’ Movements by Income Level within Urban Areas

Urban Science ◽  
2021 ◽  
Vol 5 (4) ◽  
pp. 79
Author(s):  
Sohyun Park ◽  
Aram Yang ◽  
Hui Jeong Ha ◽  
Jinhyung Lee
Keyword(s):  
Residential Mobility ◽  
Affordable Housing ◽  
Low Income ◽  
Urban Areas ◽  
Negative Binomial ◽  
Tax Credit ◽  
Mobility Patterns ◽  
Individual Level ◽  
Mixed Income ◽  
Low Income Housing

Social mixing is one of the key objectives of the housing policy in OECD countries. The Low-Income Housing Tax Credit (LIHTC) program, the largest affordable housing construction program in the US since 1986, has recently set creating mixed-income communities as one of the standards. As a project-based program, LIHTC developments are likely to influence residential mobility; however, little is known about its empirical effects. This study investigated whether new LIHTC projects are effective at attracting heterogeneous income groups to LIHTC neighborhoods, thereby contributing to creating mixed-income communities. Using unique individual-level household movement data combined with origin–destination neighborhood characteristics, we developed zero-inflated negative binomial (ZINB) models to analyze the LIHTC’s impact on residential mobility patterns in Franklin County, Ohio, US, from 2011 to 2015. The results suggest that the LIHTC attracts low-income households while deterring higher-income families, and therefore the program is not proved to be effective at creating mixed-income neighborhoods.

scholarly journals A Machine Learning Approach to the Residential Relocation Distance of Households Living in the Seoul Metropolitan Region

2018 ◽  
Author(s):  
Changhyo Yi ◽  
Kijung Kim
Keyword(s):  
Machine Learning ◽  
Residential Mobility ◽  
Ordinary Least Squares ◽  
Metropolitan Region ◽  
Learning Approach ◽  
Least Squares Regression ◽  
Mobility Patterns ◽  
Residential Relocation ◽  
Machine Learning Approach ◽  
Seoul Metropolitan Region

This study aimed to ascertain the applicability of a machine learning approach to the description of residential mobility patterns of households in the Seoul metropolitan region (SMR). The spatial range and temporal scope of the empirical study were set to 2015 to review the most recent residential mobility patterns in the SMR. The analysis data used in this study involve the microdata of Internal Migration Statistics provided by the Microdata Integrated Service of Statistics Korea. We analysed the residential relocation distance of households in the SMR by using machine learning techniques such as ordinary least squares regression and decision tree regression. The results of this study showed that a decision tree model can be more advantageous than ordinary least squares regression in terms of the explanatory power and estimation of moving distance. A large number of residential movements are mainly related to the accessibility to employment markets and some household characteristics. The shortest movements occur when households with two or more members move into densely populated districts. In contrast, job-based residential movements have relatively longer distance. Furthermore, we derived knowledge on residential relocation distance, which can provide significant information on the urban management of metropolitan residential districts and the construction of reasonable housing policies.

Sign in / Sign up

Export Citation Format

Share Document