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The Concentration and Stability of Drug Activity in Seattle, Washington Using Police and Emergency Medical Services Data

Abstract

Objectives

Prior research demonstrates that crime is highly concentrated at place and that these concentrations are stable from year to year, highlighting the importance of place to crime control and prevention. A potential limitation is that most studies only use one data source to diagnose these patterns. The present study uses data from both police and emergency medical services (EMS) to explore the spatial concentration and stability of drug activity in Seattle, Washington from 2009 to 2014.

Methods

We use concentration graphs and group-based trajectory analysis to examine concentration and stability of calls related to drug activity in both data sources separately and combined. Additionally, we employ Andresen’s S-Index to determine the similarity of concentration within the SPD data, the EMS data and the combined data year to year as well as the degree of co-location between the SPD and EMS data during the study period.

Results

We find a high degree of concentration and group-based stability for both SPD and EMS drug calls across all street segments in Seattle. Conversely, we find only moderate local geographic stability of drug use across street segments as indicated by each of the data sources over the study period. Last, we find the spatial patterns in drug use as indicated by each data source are significantly different each year.

Conclusions

At the same time these findings provide support for the law of crime concentration, they also raise questions about local stability patterns. Additionally, they highlight the importance of expanding inquiries of crime and place research into new data sources. Our results serve to reinforce the importance of multiple data sets in quantifying, understanding, and responding to the drug problem in Seattle.

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Notes

  1. The SPD data contained several classifications for narcotics calls. These included narcotics violations (sale and use), narcotics that were found/recovered, narcotics reports, narcotics connected to drug traffic/loitering, and a general classification for narcotics other. These different call types were recoded into a field for narcotics-related calls.

  2. To increase model stability, we removed all street segments from the dataset that had 0 events for all five years. Thus our largest trajectory group in each data series was not estimated by Stata, but simply reflects those streets with zero drug activity.

  3. All buffers were created using ArcGIS 10.2. Because the buffers include intersections and much of the police data have incidents reported at intersections, we also created round, 7 m buffers around the intersection points and then used the Feature Envelop to Polygon Tool to generate square buffers around the intersections. An envelope polygon provides coverage for the maximum coordinates of an area. Thus, the end result was square buffers that were 7 m from the intersection point to each side (intersections only). We then clipped the street buffers using the intersection envelope areas to obtain a file that contained buffers of just the streets (streets only). Finally, the streets buffers were merged to the enveloped intersection buffers to generate a file of street and intersection buffers combined (streets and intersections).

  4. The 95 % replacement parameter was chosen based on our geocoding accuracy with the SPD data. For all five years, the geocoding rates of the SPD drug data were 96 % or better.

  5. We chose to run the analysis for streets alone, intersections alone, and streets and intersections together given the evidence that events may operate uniquely within the different areas (see Weisburd et al. 2014).

  6. We use the term “trajectory group” throughout this section, while recognizing that some of the groups we have identified are quite small. We also recognize that our low-rate groups reflect very limited variability over time in part, because most of these streets experience no more than 1 call annually.

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Correspondence to Julie Hibdon.

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Hibdon, J., Telep, C.W. & Groff, E.R. The Concentration and Stability of Drug Activity in Seattle, Washington Using Police and Emergency Medical Services Data. J Quant Criminol 33, 497–517 (2017). https://doi.org/10.1007/s10940-016-9302-0

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Keywords

  • Drugs
  • EMS data
  • Crime places
  • Law of crime concentration
  • Stability