Introduction to Egohoods

Egohoods are an overlapping approach to constructing neighborhoods—whereas almost all other approaches to constructing neighborhoods utilize a non-overlapping approach (neighborhoods do not overlap with one another), egohoods take an explicitly spatial approach to measuring context. 

Egohoods were developed by Dr. Hipp and Dr. Boessen in 2013 and published by the journal Criminology. The paper citation and download is located below.

Hipp, John R. and Adam Boessen. 2013. “Egohoods as waves washing across the city: A new measure of “neighborhoods”.” Criminology 51:287-327.


Defining “neighborhoods” is a bedeviling challenge faced by all stud- ies of neighborhood effects and ecological models of social processes. Although scholars frequently lament the inadequacies of the various ex- isting definitions of “neighborhood,” we argue that previous strategies relying on nonoverlapping boundaries such as block groups and tracts are fundamentally flawed. The approach taken here instead builds on insights of the mental mapping literature, the social networks literature, the daily activities pattern literature, and the travel to crime literature to propose a new definition of neighborhoods: egohoods. These egohoods are conceptualized as waves washing across the surface of cities, as op- posed to independent units with nonoverlapping boundaries. This ap- proach is illustrated using crime data from nine cities: Buffalo, Chicago, Cincinnati, Cleveland, Dallas, Los Angeles, Sacramento, St. Louis, and Tucson. The results show that measures aggregated to our egohoods explain more of the variation in crime across the social environment than do models with measures aggregated to block groups or tracts. The results also suggest that measuring inequality in egohoods provides dramatically stronger positive effects on crime rates than when using the nonoverlapping boundary approach, highlighting the important new in- sights that can be obtained by using our egohood approach.


Egohoods Package for Stata

These ado files allow the researcher to take data aggregated to other units and aggregate them to egohoods.  Note that it is preferable to have the original data contained in small units such as blocks—this allows for the smoothest construction of egohoods.  If the data are in larger units, there will be an additional level of measurement error introduced to the egohoods measures.  This is not fatal—it just implies some more error.  Nearly all measures in social science research contain measurement error, and the prudent researcher might consider an analytic technique that explicitly accounts for this (e.g., structural equation modeling).

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ILSSC Papers Featuring Egohoods

ILSSC Researchers have published several papers that use Egohoods. Find out more about these publications here.

Explore the web app using Egohoods to examine Jobs-Housing Balance in Southern California

WebMapHousingEgoClick here for a web mapping application that allows you to explore job-housing balance at the neighborhood level using the 2.5 mile area around each block.  This application corresponds to MFI’s Quarterly Report “Jobs-Housing Balance in Egohoods in Southern California.”