TY - JOUR
T1 - Land Use Indexed Mobility Changes’ Impact on Urban Crimes in Metropolitan Cities
AU - Li, Yijing
AU - Sun, Ivan Y.
AU - zhang, yan
AU - Wu, Yuying
PY - 2021/9
Y1 - 2021/9
N2 - Social distancing and lockdown measures have been widely deployed in urban areas worldwide to restrict citizens’ movement to help contain the COVID-19 pandemic. This resulted in dramatic changes in people’s daily mobility, as well as the criminality and delinquency in cities. Drawing on crime data in London, Sydney, and New York in 2020, this study attempts the first one-year “look back” on the impact of massive lockdowns on crime trends in the assistance of two classic criminological theories, routine activity, and general strain, as well as cutting-edge machine learning techniques on relating the community-level geodemographics, socio-economic profiles, and mobility changes to changes in crime. The research findings suggest a general crime reduction upon mobility changes during lockdowns among the metropolitan cities, but some city-featured prominent crime types had an eye-catching increase during the period. Holistic mobility change was found to be the most crime-influential factor rather than any fine-scaled residents’ geodemographic characteristics, echoing commonly offsite criminal behaviors rather than committing crimes locally; the data-driven evidence could be further utilised for city-wide crime prediction and prevention strategies towards post-pandemic recovery.
AB - Social distancing and lockdown measures have been widely deployed in urban areas worldwide to restrict citizens’ movement to help contain the COVID-19 pandemic. This resulted in dramatic changes in people’s daily mobility, as well as the criminality and delinquency in cities. Drawing on crime data in London, Sydney, and New York in 2020, this study attempts the first one-year “look back” on the impact of massive lockdowns on crime trends in the assistance of two classic criminological theories, routine activity, and general strain, as well as cutting-edge machine learning techniques on relating the community-level geodemographics, socio-economic profiles, and mobility changes to changes in crime. The research findings suggest a general crime reduction upon mobility changes during lockdowns among the metropolitan cities, but some city-featured prominent crime types had an eye-catching increase during the period. Holistic mobility change was found to be the most crime-influential factor rather than any fine-scaled residents’ geodemographic characteristics, echoing commonly offsite criminal behaviors rather than committing crimes locally; the data-driven evidence could be further utilised for city-wide crime prediction and prevention strategies towards post-pandemic recovery.
U2 - 10.24404/615625fd16da130008eba1bf
DO - 10.24404/615625fd16da130008eba1bf
M3 - Conference paper
JO - The Evolving Scholar | IFoU 14th Edition
JF - The Evolving Scholar | IFoU 14th Edition
ER -
