scholarly journals Ambient Population and Larceny-Theft: A Spatial Analysis Using Mobile Phone Data

2020 ◽  
Vol 9 (6) ◽  
pp. 342
Author(s):  
Li He ◽  
Antonio Páez ◽  
Jianmin Jiao ◽  
Ping An ◽  
Chuntian Lu ◽  
...  
Keyword(s):  
At Risk ◽  
Spatial Analysis ◽  
Mobile Phone ◽  
Spatial Variation ◽  
Activity Patterns ◽  
Local Population ◽  
Mobile Phone Data ◽  
Alternative Measure ◽  
Population At Risk ◽  
Ambient Population

In the spatial analysis of crime, the residential population has been a conventional measure of the population at risk. Recent studies suggest that the ambient population is a useful alternative measure of the population at risk that can better capture the activity patterns of a population. However, current studies are limited by the availability of high precision demographic characteristics, such as social activities and the origins of residents. In this research, we use spatially referenced mobile phone data to measure the size and activity patterns of various types of ambient population, and further investigate the link between urban larceny-theft and population with multiple demographic and activity characteristics. A series of crime attractors, generators, and detractors are also considered in the analysis to account for the spatial variation of crime opportunities. The major findings based on a negative binomial model are three-fold. (1) The size of the non-local population and people’s social regularity calculated from mobile phone big data significantly correlate with the spatial variation of larceny-theft. (2) Crime attractors, generators, and detractors, measured by five types of Points of Interest (POIs), significantly depict the criminality of places and impact opportunities for crime. (3) Higher levels of nighttime light are associated with increased levels of larceny-theft. The results have practical implications for linking the ambient population to crime, and the insights are informative for several theories of crime and crime prevention efforts.

scholarly journals Do Mobile Phone Data Provide a Better Denominator in Crime Rates and Improve Spatiotemporal Predictions of Crime?

2021 ◽  
Vol 10 (6) ◽  
pp. 369
Author(s):  
Anneleen Rummens ◽  
Thom Snaphaan ◽  
Nico Van de Weghe ◽  
Dirk Van den Poel ◽  
Lieven J. R. Pauwels ◽  
...  
Keyword(s):  
At Risk ◽  
Mobile Phone ◽  
Risk Measure ◽  
Crime Rates ◽  
Mobile Phone Data ◽  
Policy And Practice ◽  
Predictive Policing ◽  
Population At Risk ◽  
Residential Population ◽  
Ambient Population

This article assesses whether ambient population is a more suitable population-at-risk measure for crime types with mobile targets than residential population for the purpose of intelligence-led policing applications. Specifically, the potential use of ambient population as a crime rate denominator and predictor for predictive policing models is evaluated, using mobile phone data (with a total of 9,397,473 data points) as a proxy. The results show that ambient population correlates more strongly with crime than residential population. Crime rates based on ambient population designate different problem areas than crime rates based on residential population. The prediction performance of predictive policing models can be improved by using ambient population instead of residential population. These findings support that ambient population is a more suitable population-at-risk measure, as it better reflects the underlying dynamics in spatiotemporal crime trends. Its use has therefore much as-of-yet unused potential not only for criminal research and theory testing, but also for intelligence-led policy and practice.

scholarly journals A Spatial analysis of population at risk of food insecurity using the voices from a Photovoice study

2020 ◽  
Vol 7 (2) ◽  
pp. 20-47
Author(s):  
Mikiko Terashima ◽  
Catherine Hart ◽  
Patricia Williams
Keyword(s):  
At Risk ◽  
Spatial Analysis ◽  
Urban Communities ◽  
Economic Resources ◽  
Spatial Accessibility ◽  
Community Members ◽  
Population At Risk ◽  
Food Outlets ◽  
Rural And Urban ◽  
Access To Food

To better understand community-level impacts of the built environmental quality on residents with less economic resources to acquire food, it is fruitful to combine qualitative and quantitative approaches to the investigation. We explored how the level of spatial accessibility in communities change if we incorporate even a few factors of barriers on journey to food voiced in a Photovoice study. The resulting population coverage by food outlets was dramatically reduced in both rural and urban communities, suggesting that the usual proximity-based spatial analysis likely grossly underestimate the population at risk of lacking access to food. Therefore, a ‘real’ spatial accessibility can only be understood by incorporating factors of barriers to get to food outlets, informed by the insights of community members. 

scholarly journals Fusing Mobile Phone and Travel Survey Data to Model Urban Activity Dynamics

2020 ◽  
Vol 2020 ◽  
pp. 1-17
Author(s):  
Chao Yang ◽  
Yuliang Zhang ◽  
Xianyuan Zhan ◽  
Satish V. Ukkusuri ◽  
Yifan Chen
Keyword(s):  
Mobile Phone ◽  
Survey Data ◽  
Large Scale ◽  
Activity Patterns ◽  
Mobile Phone Data ◽  
Activity Data ◽  
Travel Survey ◽  
Activity Chain ◽  
Urban Activity ◽  
Activity Dynamics

A key issue to understand urban system is to characterize the activity dynamics in a city—when, where, what, and how activities happen in a city. To better understand the urban activity dynamics, city-wide and multiday activity participation sequence data, namely, activity chain as well as suitable spatiotemporal models, are needed. The commonly used household travel survey data in activity analysis suffers from limited sample size and temporal coverage. The emergence of large-scale spatiotemporal data in urban areas, such as mobile phone data, provides a new opportunity to infer urban activities and the underlying dynamics. However, the challenge is the absence of labeled activity information in mobile phone data. Consequently, how to fuse the useful information in household survey data and mobile phone data to build city-wide, multiday, and all-time activity chains becomes an important research question. Moreover, the multidimension structure of the activity data (e.g., location, start time, duration, type) makes the extraction of spatiotemporal activity patterns another difficult problem. In this study, the authors first introduce an activity chain inference model based on tensor decomposition to infer the missing activity labels in large-scale and multiday activity data, and then develop a spatiotemporal event clustering model based on DBSCAN, called STE-DBSCAN, to identify the spatiotemporal activity patterns. The proposed approaches achieved good accuracy and produced patterns with a high level of interpretability.

scholarly journals Spatial analysis of fluoride concentrations in drinking water and population at risk in Namibia

Water SA ◽  
2017 ◽  
Vol 43 (3) ◽  
pp. 413 ◽  
Author(s):  
H Wanke ◽  
J.S. Ueland ◽  
M.H.T. Hipondoka
Keyword(s):  
At Risk ◽  
Drinking Water ◽  
Spatial Analysis ◽  
Population At Risk

The Potential GTX Travel Demand using Mobile Phone Data

2019 ◽  
Vol 7 (1) ◽  
pp. 77-84
Author(s):  
Jin Ki Eom ◽  
Kwang-Sub Lee ◽  
Ho-Chan Kwak ◽  
Ji Young Song ◽  
Myeong-Eon Seong
Keyword(s):  
Mobile Phone ◽  
Travel Demand ◽  
Mobile Phone Data

Penyelidikan KLB keracunan makanan acara ruwahan di desa Mulo, Gunung Kidul provinsi DIY

2018 ◽  
pp. 1
Author(s):  
Mur Prasetyaningrum ◽  
Z. Chomariyah ◽  
Trisno Agung Wibowo
Keyword(s):  
At Risk ◽  
Total Population ◽  
Total Coliform ◽  
Case Control ◽  
P Value ◽  
Common Source ◽  
Population At Risk ◽  
E Coli

Tujuan: Studi ini untuk mengetahui gambaran KLB keracunan pangan yang terjadi di desa Mulo menurut deskripsi epidemiologi, faktor risiko dan penyebab KLB keracunan makanan. Metode: Studi ini menggunakan studi analitik case control, dimana kasus adalah orang yang mengalami sakit pada tanggal 7 - 8 Mei 2017, tinggal di desa Mulo dan mengkonsumsi makanan olahan dari bapak S dan K. Instrument menggunakan kuesioner. Hasil: KLB terjadi di Desa Mulo RT 5 dan 6 dengan jumlah kasus sebanyak 18 orang dari total population at risk 112 orang dengan gejala utama diare (100%), mual (72,2%), demam (66,6%), pusing (66,6%) dan muntah (50%). Dari diagnosa banding menurut gejala, masa inkubasi dan agent penyebab keracunan, kecurigaan kontaminasi bakteri mengarah pada E. Coli (ETEC). Masa inkubasi 1-16 jam (rata-rata 9 jam) dan common source curve. Penyaji makanan ada dua (pak K dan pak S). Dari perhitungan AR, berdasarkan sumber makanan mengarah pada makanan dari pak S (AR=42,8%). Bedasarkan menu, perhitungan OR dan CI 95 % jenis makanan yang dicurigai sebagai penyebab KLB adalah urap/gudangan (OR=4,33; p value0,0071) dan sayur lombok (OR=6,31; p value 0,0071). Sampel yang didapatkan adalah sampel air bersih, feses, dan muntahan penderita, sampel makanan tidak didapatkan karena keterlambatan informasi dari masyarakat. Hasil laboratorium, Total Coliform sampel air bersih melebihi ambang batas, sampel feses dan muntahan mengandung bakteri Klebsiella pneumonia.Simpulan: Terdapat 3 (tiga) faktor yang diduga sebagai penyebab keracunan pada warga Desa Mulo yaitu air bersih untuk mengolah makanan tercemar bakteri patogen, pengolahan makanan tidak hygienis dan penyajian makanan pada suhu ruang lebih dari 1 jam.

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