This chapter uses annual census tract data aggregated from police records and land parcel
records, along with census data to model street-level drug markets in Pittsburgh. The dependent
variable is annual 911 calls for emergency service with the drug nature code, providing a measure
of illicit drug dealing. Traditional criminological theories suggest characteristics o f populations
correlated with crime, such as poverty, lack o f guardianship, low family status, and at-risk age
groups for modeling criminal events in areas. Ecological factors related to drug dealing are bars
and lounges, availability o f public space with low guardianship, and family public housing. Finally,
drug enforcement causes drug dealing displacement, so spatial lags of drug arrests and bar impact
raids should also contribute to drug calls.
Each group o f variables makes a contribution to explaining 9 t l drug calls, and a number o f
variables in each group is significant with expected directions of impacts. Ecological factors
provide a substantial increase in explanatory value over population characteristics, and the lagged
spatial variables eliminate spatial dependence in the estimates.
Future work will next concentrate on pooled cross-sectional time series data with monthly
observations. We believe that the before and after effects o f enforcement activities will help
delineate causal factors, using Granger (1969) causality. We plan to incorporate insights obtained
from the annual research into the new effort, including double log transformations to provide
normal errors and spatial lags of enforcement variables to eliminate spatial dependence.