A Geospatial Mixed Methods Approach to Assessing Campus Safety

Hites, Lisle; Fifolt, Matthew; Beck, Heidi; Wei, Su; Kerbawy, Shatomi; Wakelee, Jessica; Nassel, Ariann

doi:10.1177/0193841x13509815

Cited by 10 publications

(5 citation statements)

References 20 publications

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“…In the absence of a panacea to eliminate campus victimization (Su et al, 2013), attention and (re)action are urgently required. Indeed, this study offers several implications for promoting security practices and policies that should be considered by the different stakeholders involved in campus security (Fox et al, 2009;Hart & Colavito, 2011;Volkwein et al, 1995).…”

Section: Resultsmentioning

confidence: 99%

Campus Criminal Victimization among Higher Education Students: A Diagnosis of Local Security in Porto

Sani

Nunes

Azevedo

et al. 2019

Journal of Criminal Justice Education

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This study addressed and characterized direct and indirect criminal victimization among college students and examined the associations between victimization and other variables. The participants were 775 students of both genders with a mean age of 21.76 years. Data were collected through self-reports using the "Diagnosis of Local Security Questionnaire". Overall, 8.6% of the students reported direct victimization, and 39.7% reported indirect victimization. The most reported crimes were robbery and theft, while the least prevalent were sexual offense, domestic violence, and fraud. Most incidents involved a stranger and occurred at night in the street. Direct victimization was associated with gender, age, marital status, student level, attendance status, and institution domain. There were significant associations between indirect victimization and nationality, student type, attendance status, and institution domain. The perception of (in)security was significantly associated with victimization. Therefore, physical and social measures are necessary to reduce crime and increase security. ARTICLE HISTORY

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Section: Resultsmentioning

confidence: 99%

Campus Criminal Victimization among Higher Education Students: A Diagnosis of Local Security in Porto

Sani

Nunes

Azevedo

et al. 2019

Journal of Criminal Justice Education

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“…Many researchers have addressed the issue of safety analysis using GIS techniques, ranging from pedestrian falls, traffic crashes, and crimes to safety perceptions (Ceccato and Willems, 2019;Hu et al, 2012;Nobles et al, 2010;Kyttä et al, 2014). Other studies integrated perception data into safety analysis regarding crime risk (Hites et al, 2013) and other traffic-related incidents (Schneider et al, 2001).…”

Section: The Potential Of Ppgis For the Study Of Safety On Campusmentioning

confidence: 99%

“…In 2015, a female college student at Central Lake Park in the Guangzhou university town was murdered on campus (Zheng, 2016). This case was widely disseminated among students by social media and quickly helped shed light on current safety challenges that universities worldwide face, from sexual assault and traffic incidents to crime (Fedina et al, 2018;Hites et al, 2013). Although safety on campus has been a growing research field (Jennings et al, 2007), most past and current studies lack a holistic perspective on safety.…”

Section: Introductionmentioning

confidence: 99%

Safety perceptions in university campuses: the role of environment

Huang

Ceccato

Kyttä

2022

Crime Prev Community Saf

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This study reports the variation in safety perceptions of university students (N = 196 university students) on university campuses in South China, focusing on the risk of crime and traffic incidents. Using a public participation geographic information system, we explore potential links between students’ perceptions and campus characteristics. We conclude that environmental cues associated with the risk of crime victimisation (e.g. lack of natural surveillance) are more often linked to poorer safety perceptions than those linked to the risk of traffic conditions. Although half of the students declare feeling safe on campus, their perception varies by individual characteristics, frequency of use, and daily experiences. Inspections of campuses environments using a door-to-door perspective are advised when safety becomes an integral part of sustainability planning of universities worldwide.

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“…Further investigation using figures and maps were needed since Global autocorrelations indicate a clustering effect (positive spatial autocorrelation) on solid fuel use over the country. Therefore, hotspot analysis (Gettis-Ord Gi*) was performed to PLOS ONE identify patterns of spatial variation [27] and emphasize the previously stated using global autocorrelations (cluster effect) on solid fuel use. Cluster and outlier analysis (Anselin local Moran's I) was used to describe the spatial patterns of the dependent variables (solid fuel use).…”

Section: Predictor Variablesmentioning

confidence: 99%

Spatial variation and determinants of solid fuel use in Ethiopia; Mixed effect and spatial analysis using 2019 Ethiopia Mini Demographic and Health Survey dataset

Azanaw,

Sisay Chanie

2023

PLoS ONE

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Background According to UNSD, World Bank, and WHO, a 2020 joint report, 3 billion people who used solid fuel were found in low and middle-income countries. The burning of such type of fuel emits a variety of pollutants such as PM2.5, PM10, CO, NO2, organic compounds, and other substances which a had wide range of public health problems The evidence from the WHO report, indoor air pollution was attributed to around 3.2 million deaths each year in 2020, and more than 237 000 deaths of children under the age of five. This study aims to investigate solid fuel prevalence, predictors, and spatial variation in Ethiopia. Method This study was conducted in Ethiopia based on the fifth Ethiopian Demographic and Health Surveys 2019 dataset. 8,663 households were successfully interviewed at a response rate of 99%. Weighted by sampling weight was done to do a reliable statistical analysis. Fuel type was used as the outcome variable whereas sex of household head (male or female), wealth index (poor, middle, and rich), educational status (no education, primary, secondary, higher), having television and radio, a separate room used as a kitchen, were individual predictor variables and community level education(lower/higher), residence(urban/rural), community level media exposure (exposed/unexposed), region (pastoralist, semi-pastoralist, Agrarian, and City administration) were community level variables. All the above analyses were conducted using Excel Microsoft 2016, STATA 14, ArcGIS 10.7, and SaTScan 10.1 software. Results The majority (72.62%) of household heads were males. The overall national level of solid fuel use was 87.13% (95% CI (86.4%-87.82%)). From this 87.13% of solid fuel use, 18.60% accounts for urban and 68.53% for rural parts of the country. Educational status, having television, accessing electricity, and wealth index were individual-level variables community-level education, type of residence, and region were community-level variables significantly associated factors towards solid fuel use in our study. Global (z-score = 33.109144, P-value <0.0001), local (hot spot, cluster, and outlier), and Spatial Scan statistical analyses revealed that there was a major geographical variation in solid fuel use across Ethiopia. Conclusion Based on this finding, the prevalence of solid fuel use was higher in Ethiopia. Educational status, having television, accessing electricity, and wealth index were individual-level variables and community-level education, type of residence, and region were community-level variables statistically significant factors in determining fuel choice for cooking. There was significant spatial variation in the prevalence of solid-fuel use across the country. In order to addressing such heavily dependent on solid fuel use, responsible bodies like health policy makers, national and international organizations, and public health researchers should work on showing health problems of solid fuel use and the means of increasing clean fuel use. Substantial policy modifications are desirable to reach access to clean fuels and technologies (SDG 7.1.2) by 2030 to address health inequities.

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A Geospatial Mixed Methods Approach to Assessing Campus Safety

Cited by 10 publications

References 20 publications

Campus Criminal Victimization among Higher Education Students: A Diagnosis of Local Security in Porto

Campus Criminal Victimization among Higher Education Students: A Diagnosis of Local Security in Porto

Safety perceptions in university campuses: the role of environment

Spatial variation and determinants of solid fuel use in Ethiopia; Mixed effect and spatial analysis using 2019 Ethiopia Mini Demographic and Health Survey dataset

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