scholarly journals Do Migrant and Native Robbers Target Different Places?

2021 ◽  
Vol 10 (11) ◽  
pp. 771
Author(s):  
Dongping Long ◽  
Lin Liu
Keyword(s):  
Urban Areas ◽  
Location Choice ◽  
Criminological Theory ◽  
Mobile Phone Data ◽  
Spatial Awareness ◽  
Location Choices ◽  
Spatial Choice ◽  
Sports Stadiums ◽  
Spatial Differences ◽  
Subway Stations

The spatial pattern of crime has been a central theme of criminological research. Recently, the spatial variation in the crime location choice of offenders by different population groups has been gaining more attention. This study addresses the issue of whether the spatial distribution of migrant robbers’ crime location choices is different from those of native robbers. Further, what factors contribute to such differences? Using a kernel density estimation and the discrete spatial choice modeling, we combine the offender data, POI data, and mobile phone data to explain the crime location choice of the street robbers who committed offenses and were arrested from 2012 to 2016 in ZG City, China. The results demonstrate that the crime location choices between migrant robbers and native robbers have obvious spatial differences. Migrant robbers tend to choose the labor-intensive industrial cluster, while native robbers prefer the old urban areas and urban villages. Wholesale markets, sports stadiums, transportation hubs, and subway stations only affect migrant robbers’ crime location choices, but not native robbers’. These results may be attributable to the different spatial awareness between migrant robbers and native robbers. The implications of the findings for criminological theory and crime prevention are discussed.

ANALYSIS OF COMMUTE CHARATERISTICS AND RESIDENTIAL LOCATION CHOICE OF IIUM GOMBAK CAMPUS EMPLOYEES OF MALAYSIA

2010 ◽  
Vol 8 ◽  
Author(s):  
Mohammad Abdul Mohit ◽  
Mootaz Munjid Mustafa
Keyword(s):  
Urban Areas ◽  
Location Choice ◽  
Residential Location ◽  
Higher Learning ◽  
Kuala Lumpur ◽  
Residential Location Choice ◽  
North West ◽  
The North ◽  
Residential Characteristics ◽  
Learning Institutions

Higher learning institutions, particularly uni versities, are important nodes which can help in decentralizing the monocentric stigma of urban areas by encouraging employment and housing growth in metropolitan areas. The case study Gombak Campus of international Islamic University Malaysia (IIUM), located 15 kilometres to the north-west of Kuala Lumpur City, is currently an employment node in the Klang Valley region. Being a node of employment, it is expected to generate residential development in the vicinity of its location by supporting the determining two fac tors of residential location - commuting cost and rent. Although there are certain truths that rent and commute cost are important determinants in households' residential location, other factors also influence residential location decision making. This paper, therefore, attempts to identify an array of factors and the extent to which these factors influence commute and residential attributes of the employees of IIUM Gombak Campus. Findings of this study reveal that there is a significant relationship between commute behaviour and residential characteristics and a number of other factors nonnally overlooked by the mainstream residential location choice models.

Contextual Density and US Automotive CO2 Emissions across the Rural–Urban Continuum

2015 ◽  
Vol 40 (6) ◽  
pp. 590-615 ◽  
Author(s):  
Andrew Perumal ◽  
David Timmons
Keyword(s):  
Urban Areas ◽  
Methodological Approach ◽  
Fuel Efficiency ◽  
Geographical Information ◽  
Population Densities ◽  
Spatial Differences ◽  
Using Data ◽  
Household Travel ◽  
Vehicle Fuel Efficiency ◽  
National Household Travel Survey

Using data from the 2009 National Household Travel Survey, we quantify the effects of settlement patterns on individual driving habits and the resulting automotive carbon dioxide (CO2) emissions. We employ CO2 emissions to capture this impact accurately, as it reflects both vehicle miles traveled and any spatial differences in vehicle fuel efficiency choices. While previous studies have compared automotive travel in urban and suburban areas, our approach characterizes emissions across the entire US rural–urban gradient, focusing on the effects of population density. Rather than using categorical measures of contextual density (city, suburb, town, etc.), we use a geographical information system to calculate continuous measures of contextual density, that is, density at different proximities to households. These measures of contextual density allow us to model travel effects induced by the gravitational pull of the population densities of urban cores. Further, our methodological approach frames location choice as an endogenous treatment effect; that is, residential locations are not randomly assigned across our sample and significantly alter driving behavior. We find that individuals living in urban cores generate the lowest per capita automotive CO2 emissions, due to close proximities of population concentrations. Rather than attracting individuals who would likely have low CO2 emissions anyway, urban location apparently mitigates the emissions of people who would otherwise tend to have high automotive CO2 emissions. We find larger elasticities with respect to density than previous studies and also find that the attractive forces of population densities affect driving patterns at distances up to sixty-one kilometers outside of urban areas.

scholarly journals Mobile Phone Data for Urban Climate Change Adaptation: Reviewing Applications, Opportunities and Key Challenges

2020 ◽  
Vol 12 (4) ◽  
pp. 1501
Author(s):  
Sébastien Dujardin ◽  
Damien Jacques ◽  
Jessica Steele ◽  
Catherine Linard
Keyword(s):  
Climate Change ◽  
Urban Planning ◽  
Mobile Phone ◽  
Urban Areas ◽  
Urban Governance ◽  
Urban Climate ◽  
Spatial Modelling ◽  
Mobile Phone Data ◽  
Transformative Change ◽  
Digital Information

Climate change places cities at increasing risk and poses a serious challenge for adaptation. As a response, novel sources of data combined with data-driven logics and advanced spatial modelling techniques have the potential for transformative change in the role of information in urban planning. However, little practical guidance exists on the potential opportunities offered by mobile phone data for enhancing adaptive capacities in urban areas. Building upon a review of spatial studies mobilizing mobile phone data, this paper explores the opportunities offered by such digital information for providing spatially-explicit assessments of urban vulnerability, and shows the ways these can help developing more dynamic strategies and tools for urban planning and disaster risk management. Finally, building upon the limitations of mobile phone data analysis, it discusses the key urban governance challenges that need to be addressed for supporting the emergence of transformative change in current planning frameworks.

scholarly journals Examining Spatial Heterogeneity Effects of Landscape and Environment on the Residential Location Choice of the Highly Educated Population in Guangzhou, China

2020 ◽  
Vol 12 (9) ◽  
pp. 3869
Author(s):  
Yang Wang ◽  
Kangmin Wu ◽  
Jing Qin ◽  
Changjian Wang ◽  
Hong’ou Zhang
Keyword(s):  
Spatial Heterogeneity ◽  
Location Choice ◽  
Reference Value ◽  
Residential Location ◽  
Location Choices ◽  
Residential Location Choice ◽  
Population Proportion ◽  
Highly Educated ◽  
Global Regression ◽  
Heterogeneity Effects

The residential location choice of the highly educated population is an important consideration to construct a livable city. While landscape and environment are important factors, few studies have deeply analyzed the spatial heterogeneity effects of landscape and environment on the residential location choices of a highly educated population. Taking Guangzhou as the sample, we built a livability-oriented conceptual framework of landscape and environment, and constructed datasets for highly educated population proportion, landscape, and environment factors, and other influencing factors for Guangzhou’s 1364 communities. Global regression and geographically weighted regression (GWR) models are used for analysis. The GWR model is more effective than the global regression model. We found spatial heterogeneity in the strength and direction of the relationship between the highly educated population proportion and landscape and environment. We find that landscape and environment exert spatial heterogeneity effects on the residential location choice of the highly educated population in Guangzhou. The conclusions will be of reference value to further understand how the spatial limitations of landscape and environment affect residential location choices. This study will help city managers formulate spatially differentiated environment improvement policies, thereby increasing the city’s sustainable development capabilities.

Influence of Accessibility on Residential Location Choice

2005 ◽  
Vol 1902 (1) ◽  
pp. 63-70 ◽  
Author(s):  
Barry Zondag ◽  
Marits Pieters
Keyword(s):  
The Netherlands ◽  
Housing Market ◽  
Transport System ◽  
Location Choice ◽  
Residential Location ◽  
Significant Variable ◽  
Location Choices ◽  
Residential Location Choice ◽  
Residential Moves ◽  
Time Variables

There has been substantial discussion among planners about the influence of transport in residential location choices. The purpose of this paper is to analyze the importance of accessibility in explaining residential location choices. The paper addresses this issue by presenting and analyzing findings from the literature and results of a housing market estimation study in the Netherlands. The research findings for the Netherlands illustrate that the transport system influences residential moves at three stages: in move–stay choice, estimation results show that households are less likely to move away from a more accessible location; travel time variables are significant for all household types, and therefore changes in the transport system will affect the size of the housing market and search area of the households; the model estimation results suggest that accessibility of a specific location for many household types is not a significant variable in their location choice. Overall, the empirical results suggest that the role of accessibility is significant but small compared with the effect of demographic factors, neighborhood amenities, and dwelling attributes in explaining residential location choices. The empirical findings are confirmed by findings in the literature; the present results are located at the lower end of findings reported in the literature. An important factor contributing to this result is that accessibility changes among regions in the Netherlands are rather small.

scholarly journals Understanding Spatial and Temporal Change Patterns of Population in Urban Areas Using Mobile Phone Data

2020 ◽  
Vol 145 ◽  
pp. 02007
Author(s):  
Tianwen Liang ◽  
Huan Liu ◽  
Zheng Zhang
Keyword(s):  
Land Use ◽  
Principal Components ◽  
Urban Areas ◽  
Population Change ◽  
Temporal Change ◽  
Geographical Area ◽  
Mobile Phone Data ◽  
Population Changes ◽  
Step Method ◽  
Eigen Decomposition

The wide application of information computing technology has allowed for the emergence of big data on tracing human activities. Therefore, it provides an opportunity to explore temporal profile of population changes in geographical area subdivisions. In this paper, we present a multi-step method to characterize and approximate temporal changes of population in a geographical area subdivision using eigen decomposition. Datasets in weekday and weekend are decomposed to obtain the principal temporal change profiles in Xiamen, China. The Principal Components are common patterns of temporal population changes shared by most geographical area subdivisions. Its corresponding elements in eigenvectors could be regard as a coefficient to principal components. Then, a measure, which is the similarity of each eigenvector to a basis vector, that could characterize the temporal population change is established. Based on this, the coupling interaction between population changes and land use characteristics is explored using this measure. It shows that it is restricted by land use characteristics and also is a reflection of population changes over time. These results provided an insight on understanding temporal population change patterns and it would help to improve urban planning and establish a job-housing balance.

scholarly journals Assessing the use of mobile phone data to describe recurrent mobility patterns in spatial epidemic models

2017 ◽  
Vol 4 (5) ◽  
pp. 160950 ◽  
Author(s):  
Cecilia Panigutti ◽  
Michele Tizzoni ◽  
Paolo Bajardi ◽  
Zbigniew Smoreda ◽  
Vittoria Colizza
Keyword(s):  
Infectious Disease ◽  
Mobile Phone ◽  
Urban Areas ◽  
Large Scale ◽  
Human Mobility ◽  
Mobile Phone Data ◽  
Disease Models ◽  
Mobility Patterns ◽  
Mobility Data ◽  
Infectious Disease Models

The recent availability of large-scale call detail record data has substantially improved our ability of quantifying human travel patterns with broad applications in epidemiology. Notwithstanding a number of successful case studies, previous works have shown that using different mobility data sources, such as mobile phone data or census surveys, to parametrize infectious disease models can generate divergent outcomes. Thus, it remains unclear to what extent epidemic modelling results may vary when using different proxies for human movements. Here, we systematically compare 658 000 simulated outbreaks generated with a spatially structured epidemic model based on two different human mobility networks: a commuting network of France extracted from mobile phone data and another extracted from a census survey. We compare epidemic patterns originating from all the 329 possible outbreak seed locations and identify the structural network properties of the seeding nodes that best predict spatial and temporal epidemic patterns to be alike. We find that similarity of simulated epidemics is significantly correlated to connectivity, traffic and population size of the seeding nodes, suggesting that the adequacy of mobile phone data for infectious disease models becomes higher when epidemics spread between highly connected and heavily populated locations, such as large urban areas.

Mobile Phones as a Lens into Slum Dynamics

2012 ◽  
pp. 334-352
Author(s):  
Amy Wesolowski ◽  
Nathan Eagle
Keyword(s):  
Mobile Phone ◽  
Mobile Phones ◽  
Urban Areas ◽  
Large Scale ◽  
Mobile Phone Data ◽  
Data Sets ◽  
Location Data ◽  
Planning Decisions ◽  
Large Scale Data ◽  
Slum Dwellers

The worldwide adoption of mobile phones is providing researchers with an unprecedented opportunity to utilize large-scale data to better understand human behavior. This chapter highlights the potential use of mobile phone data to better understand the dynamics driving slums in Kenya. Given slum dwellers informal and transient lifetimes (in terms of places of employment, living situations, etc.), comprehensive longitude behavioral data sets are rare. Working with communication and location data from Kenya’s leading mobile phone operator, the authors use mobile phone data as a window into the social, mobile, and economic dimensions of slum dwellers. The authors address questions about the functionality of slums in urban areas in terms of economic, social, and migratory dynamics. In particular, the authors discuss economic mobility in slums, the importance of social networks, and the connectivity between slums and other urban areas. With four years until the 2015 deadline to meet the Millennium Development Goals, including the goal to improve the lives of slum dwellers worldwide, there is a great need for tools to make development and urban planning decisions more beneficial and precise.

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