311 service requests as indicators of neighborhood distress and opioid use disorder

New paper: Li, Y., Hyder, A., Southerland, L.T., Hammond, G., Porr, A. and Miller, H.J. “311 service requests as indicators of neighborhood distress and opioid use disorder,” Scientific Reports, 10, 19579.

Abstract

Opioid use disorder and overdose deaths is a public health crisis in the United States, and there is increasing recognition that its etiology is rooted in part by social determinants such as poverty, isolation and social upheaval. Limiting research and policy interventions is the low temporal and spatial resolution of publicly available administrative data such as census data. We explore the use of municipal service requests (also known as “311” requests) as high resolution spatial and temporal indicators of neighborhood social distress and opioid misuse. We analyze the spatial associations between georeferenced opioid overdose event (OOE) data from emergency medical service responders and 311 service request data from the City of Columbus, OH, USA for the time period 2008–2017. We find 10 out of 21 types of 311 requests spatially associate with OOEs and also characterize neighborhoods with lower socio-economic status in the city, both consistently over time. We also demonstrate that the 311 indicators are capable of predicting OOE hotspots at the neighborhood-level: our results show code violation, public health, and street lighting were the top three accurate predictors with predictive accuracy as 0.92, 0.89 and 0.83, respectively. Since 311 requests are publicly available with high spatial and temporal resolution, they can be effective as opioid overdose surveillance indicators for basic research and applied policy.

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Central Ohio Regional Sustainability Dashboard

Central Ohio now has an interactive, online dashboard that provides current and accurate information on the 15-county region’s sustainability accomplishments.

The Mid-Ohio Regional Planning Commission (MORPC) and the Center for Urban and Regional Analysis (CURA) at The Ohio State University are launching an interactive, online dashboard that provides current and accurate information on Central Ohio’s sustainability. You can select, visualize, map and download data on a wide range of sustainability indicators for Central Ohio.

The dashboard serves as the official status report for Central Ohio’s progress toward the Regional Sustainability Agenda, which sets the framework for communities and regional partners to work toward common goals. It was created out of the need for greater access to data and information in order to shed light on the impacts of collective sustainability efforts region.

MORPC press release

Dashboard: https://rsd.morpc.org/

 

Does real-time transit information reduce waiting time? An empirical analysis

New paper: Liu, L. and Miller, H.J. (2020) “Does real-time transit information reduce waiting time? An empirical analysis,” Transportation Research A, 141, 167-179.

Highlights

  • Public transit agencies publish real time information for use in mobile apps
  • We benchmark several strategies using empirical transit system performance data.
  • Overall, real time information does not outperform simply following schedule.
  • Real time information can reduce waiting time for some users based on location.
  • Including a time buffer improves the greedy approach used by popular apps.

Abstract

A claimed benefit of real-time information (RTI) apps in public transit systems is the reduction of waiting time by allowing passengers to appropriately time their arrivals at transit stops. Although previous research investigated the overall impact of RTI on waiting time, few studies examine the mechanisms underlying these claims, and variations in its effectiveness over time and space. In this paper, we theorize and validate the sources of RTI-based users’ waiting time penalties: reclaimed delay (bus drivers compensating for being behind schedule) and discontinuity delay (an artifact of the update frequency of RTI). We compare two RTI-based strategies – the greedy strategy used by popular trip planning apps and a prudent strategy with an insurance buffer – with non-RTI benchmarks of arbitrary arrival and following the schedule. Using real-time bus location data from a medium-sized US city, we calculate the empirical waiting times and risk of missing a bus for each trip planning strategy. We find that the best RTI strategy, a prudent tactic with an optimized insurance time buffer, performs roughly the same as the simple, follow-the-schedule tactic that does not use RTI. However, relative performance varies over time and space. Moreover, the greedy tactic in common transit apps is the worst strategy, even worse than showing up at a bus stop arbitrarily. These results suggest limitations on claims that RTI reduces public transit waiting times.

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