Investigating crime harm index in the low and downward crime contexts: A spatio-temporal analysis of the Japanese Crime Harm Index

Ohyama, Tomoya; Hanyu, Kazunori; Tani, Masayuki; Nakae, Momoka

doi:10.1016/j.cities.2022.103922

Cited by 7 publications

(4 citation statements)

References 41 publications

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“…The Danish CHI follows the same methodology as other CHI models worldwide (see variations and applications in Ariel et al ., 2016; Basford et al ., 2021; Bland et al ., 2021; Curtis-Ham, 2022; Curtis-Ham and Walton, 2017, 2018; Dudfield et al ., 2017; House and Neyroud, 2018; Kärrholm et al. , 2020; Linton and Ariel, 2020; Macbeth and Ariel, 2019; Mitchell, 2019; Ohyama et al ., 2022; Ransley et al ., 2018; Sidhu et al ., 2017; Šimon and Jíchová, 2020; Weinborn et al ., 2017) and is considered an objective and valid measure of crime harm (however, cf. Sarnecki, 2021).…”

Section: Methodsmentioning

confidence: 99%

“…The CHI assigns each crime category a harm score based on the Danish sentencing guidelines (the guidance to prosecutors and judges as to the recommended number of prison days first-time offenders should receive if the case were to progress to PIJPSM 46,2 court). The Danish CHI follows the same methodology as other CHI models worldwide (see variations and applications in Ariel et al, 2016;Basford et al, 2021;Bland et al, 2021;Curtis-Ham, 2022;Curtis-Ham andWalton, 2017, 2018;Dudfield et al, 2017;House and Neyroud, 2018;K€ arrholm et al, 2020;Linton and Ariel, 2020;Macbeth and Ariel, 2019;Mitchell, 2019;Ohyama et al, 2022;Ransley et al, 2018;Sidhu et al, 2017;Simon and J ıchov a, 2020;Weinborn et al, 2017) and is considered an objective and valid measure of crime harm (however, cf. Sarnecki, 2021).…”

Section: Measurementsmentioning

confidence: 99%

See 1 more Smart Citation

A simple metric for predicting repeated intimate partner violence harm based on the level of harm of the index offence (… as long as a non-linear statistic is applied)

Loewenstein

Ariel

Harinam

et al. 2023

PIJPSM

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PurposeA recent body of evidence investigated repeated intimate partner violence (IPV) using crime harm indices (the severity of victimisation), instead of crime counts (the number of additional victimisation incidents). Yet, the predictive utility of harm scores in IPV remains unclear – except that high-harm IPV is not usually followed by any additional IPV incidents. The authors take cases of repeat IPV from North Zealand Police, Denmark, to predict subsequent IPV harm and counts based on the level of harm of the first reported IPV offence.Design/methodology/approachUsing the Danish crime harm index (CHI) to estimate harm levels, non-linear regression models are applied (due to the non-linear nature of the data) to show that the CHI level of the index offence validly predicts gains in future CHI but does not predict IPV counts.FindingsThe findings suggest that whilst high-harm IPV is a rare event and repeat high-harm IPV even rarer, when they do occur, escalation in harm is likely to occur.Practical implicationsA simple metric of harm of the first reported IPV offence can validly predict future harm – however, scholars should apply more fitting analytical techniques than crude descriptive statistics, which fail to take into account the non-linear distribution of police records.Originality/valueThis is the first study to show the value of predicting future harm based on prior harm in IPV.

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Section: Methodsmentioning

confidence: 99%

Section: Measurementsmentioning

confidence: 99%

A simple metric for predicting repeated intimate partner violence harm based on the level of harm of the index offence (… as long as a non-linear statistic is applied)

Loewenstein

Ariel

Harinam

et al. 2023

PIJPSM

View full text Add to dashboard Cite

show abstract

“…Space–time analysis of bars and crime reveals a link. Much time has passed since Louis Thurstone compared different types of crime almost a century ago as stated by Ohyama et al [ 2 ]. Recent research has used methodologies such as the Cambridge Crime Harm Index (CCHI), which assesses the detrimental effects of crime on society and identifies places with high crime harm by assigning a greater weight to the more serious offenses.…”

Section: Related Workmentioning

confidence: 99%

Spatio-temporal Crime Analysis and Forecasting on Twitter Data Using Machine Learning Algorithms

Vivek

Prathap

2023

SN COMPUT. SCI.

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The concept of social media began to gain popularity in the late 1990s and has played a significant role in connecting people across the globe. The constant addition of features to old social media platforms and the creation of new ones have helped amass and retain an extensive user base. Users could now share their views and provide detailed accounts of events from worldwide to reach like-minded people. This led to the popularization of blogging and brought into focus the posts of the commoner. These posts began to be verified and included in mainstream news articles bringing about a revolution in journalism. This research aims to use a social media platform, Twitter, to classify, visualize, and forecast Indian crime tweet data and provide a spatio-temporal view of crime in the country using statistical and machine learning models. The Tweepy Python module's search function and '#crime' query have been used to scrape relevant tweets under geographical constraints, followed by substring-keyword classification using 318 unique crime keywords. The Bokeh and gmaps Python modules create analytical and geospatial visualizations, respectively. Time series forecasting of crime tweet count is performed by comparing the accuracy of Long Short-Term Memory (LSTM), Auto-Regressive Integrated Moving Average (ARIMA), and Seasonal Auto-Regressivee Integrated Moving Average (SARIMA) models to determine the best model.

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“…Eğer indeks değeri sıfırdan küçükse (negatif değerler), veri noktaları dağınık durumdadır. Eğer indeks değeri sıfıra çok yakın veya sıfırsa, veri noktaları rastgele dağılmıştır (Chen & Yang, 2018;Cheruiyot, 2022;Ohyama, Hanyu, Tani, & Nakae, 2022).…”

Section: çAlışma Alanıunclassified

Türkiye’de 2015 ile 2022 yılları arasında meydana gelen orman yangınlarının coğrafi bilgi sistemleri ile zamansal ve mekânsal analizi

Demirel,

Türk

2023

J Geod Geoinf

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Öz: Dünyada orman yangınlarının can ve mal kaybına neden olan sosyal, ekonomik ve ekolojik olmak üzere maddi ve manevi birçok sonuçları vardır. Son yıllarda dünya genelinde orman yangınlarında hem sıklık hem de büyüklük açısından önemli bir artış gözlemlenmektedir. Bu nedenle ekolojik düzenin istikrarlı bir şekilde devam etmesi için orman yangınlarının analiz edilmesi ve buna karşı gerekli tedbirlerin alınması son derece önemlidir. Bu bağlamda orman yangınlarının zaman içerisindeki mekânsal dağılımının modellenmesi ve risk faktörü oluşturan bölgelerin incelenmesi kritik öneme sahiptir. Bu çalışmada Türkiye'de 2015 ile 2022 yılları arasında meydana gelen orman yangınlarının il sınırı seviyesinde mekânsal ve zamansal dağılımı yangının coğrafi konum ve çıkış nedeni arasındaki ilişki dikkate alınarak Coğrafi Bilgi Sistemi ortamında analiz edilmiş ve mekânsal istatistiksel testler (Global Moran's I, AnselinLocal Moran's I ve Getis-Ord Gi*) kullanılarak yıl bazındaki kümelenmeler ortaya konmuştur. Böylece orman yangınlarına yönelik alınması gereken önlemler ve karar vericilere sağlanacak destekler konularında önemli sonuçlar elde edilmiştir.

show abstract

Investigating crime harm index in the low and downward crime contexts: A spatio-temporal analysis of the Japanese Crime Harm Index

Cited by 7 publications

References 41 publications

A simple metric for predicting repeated intimate partner violence harm based on the level of harm of the index offence (… as long as a non-linear statistic is applied)

A simple metric for predicting repeated intimate partner violence harm based on the level of harm of the index offence (… as long as a non-linear statistic is applied)

Spatio-temporal Crime Analysis and Forecasting on Twitter Data Using Machine Learning Algorithms

Türkiye’de 2015 ile 2022 yılları arasında meydana gelen orman yangınlarının coğrafi bilgi sistemleri ile zamansal ve mekânsal analizi

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