Motivation for the Analysis

This report outlines a statistical prediction/inference framework and modeling structure for spatially referenced child maltreatment events in Little Rock between 2015 and 2018. The underlying premise is to aggregate multiple spatial events to a common underlying geography by looking at the counts of events in pre-defined raster cells, and build a statistical or machine learning framework, that accounts for both social and environmental (physical - built) factors as well as the spatial clustering of both the response events and other factors and resources.

To visualize the extent of spatial clustering, the figure below displays the count of maltreatment events between July 2015 and June 2018 by census tracts. This figure demonstrates that the spatial distribution of child maltreatment is far from uniformly distributed across tracts. Thus, a critical question is allocation of limited child welfare resources to the communities that are most severely affected.

To identify locations that will need the most resources we must first determine where child maltreatment events are clustered. While individual, family, and household level factors affect child maltreatment, extant research suggests that community and social factors play an important role in understanding where maltreatment may occur .

The maltreatment event clusters are visualized below which maps the rate of child maltreatment events in Little Rock, AR between 2015 and 2018. Recent work indicates that variation in these spatial clusters can be predicted by environmental factors such as crime, blight, and bars and restaurants.

Exploratory Analysis

During this process, a number of descriptive insights from the data are derived. To begin, maltreatment events across space and time are visualized and possible selection bias in the data is discussed. Next, hypotheses related to the clustering of maltreatment events across space are presented. Finally, a series of pairwise correlations between maltreatment and the features.

Testing maltreatment events for clusters

One overarching assumption behind the model is that maltreatment events are clustered in space. To test this hypothesis, the Local Moran’s I statistic is employed, comparing CPS count by fishnet to Local Moran's I value: the result shows areas that resemble discrete clusters of maltreatment in space.

Spatial Autocorrelation via Moran's I

Feature Engineering

In this section we seek to understand the extent of the linear relationship between the available features. Towards this end, we first create several features from the available data (i.e. built environment factors and census data). The two figures below visualize pairwise correlations for the most correlative risk and protective factors, respectively. Note the correlation coefficients associated with maltreatment count (cps_net) and maltreatment rate (cps_rate). The colors of the plot vary with the strength of the correlations, either positive or negative.

There are 3 different prefixes associated which each type of feature. NN refers to features calculated by taking the average distance between a fishnet grid cell and its n nearest risk/protective factor neighbor. ed refers to the Euclidean distance between a fishnet grid cell and its 1 nearest risk/protective factor neighbor. agg refers to the count of risk/protective factor events in a given fishnet grid cell.

Correlation of Risk Features

 

Correlation of Protective Features

Risk Factors

The following maps highlight a sampling of where potential risk factors are located throughout Little Rock (click to view)

Banks Barber and Beauty Shops Bus Stops Gas and Convenience Stores Hotels and Motels Liquor Stores Mobile Homes Rentals Restaurants and Bars Tobacco Sales