The document describes a study that uses location-based social network (LBSN) data from Foursquare to measure ambient population dynamics in neighborhoods and relate this to long-term crime levels. Census data and Foursquare data are used to craft metrics representing resident population and ambient population, respectively. A spatial econometrics model finds that both the novel LBSN factors and established census factors are significantly related to crime levels, with a combined model performing best. This provides support for theories about "eyes on the street" and criminogenic places.

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Cristina Kadar, Raquel Rosés, Irena Pletikosa
PhD candidate – Information Management, D-­MTEC, ETH Zurich
www.im.ethz.ch/people/ckadar
@tweeting_cris
# spatial/social data science, # urban computing, # information systems
Measuring ambient population from location-­based social networks (LBSN)
to describe urban crime

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Social disorganization theory: ecological attributes of the neighborhood
15.09.2017 2
Shaw, Sampson, ...: Social disorganization theory
Census data:
static & obsolete
Resident Population

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Eyes on the street and criminogenic places: population dynamics in the neighborhood
15.09.2017 3
LBSN data:
location, time, context
Jane Jacobs: Eyes on the street
Branthingham & Branthingham:
Crime attractors & crime generators
Ambient Population

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Previous work in the Computational Social Science and Data Mining communities using
human dynamics data for crime modelling
§ Simple correlations between crime rates and some diversity metrics from mobile phone data [Traunmueller
et al., SocInfo ’14]
§ Using machine learning techniques to predict short-­term crime using features crafted from:
§ Twitter data [Gerber, Decision Support Systems ‘14]
§ mobile phone data [Bogomolov et al., ICMI ’14]
§ POI and taxi data [Wang et al., KDD’16]
Spatial econometrics models that produce a multivariate, yet interpretable model
Compare & contrast the (statistically significant!) contribution of the resident and ambient population
Use census and Foursquare data

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2011 2015
neighborhood (= census tract)
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Total crime counts in N = 2167 neighborhoods
Long-­term crime in New York City is analyzed at neighborhood level

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Craft suitable counterparts of the established resident population metrics when using
LBSN proxies for the ambient population!
Resident Population Ambient Population
Control area area
Density population venues/check-­ins
Diversity racial-­ethnic, income-­based activity-­based
Risk stable population
vacant & rented households
activity hot spots
Social Disorganization Theory (Sampson, …) Eyes on the street (Jacobs)
Crime attractors & generators (Brantingham^2)

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Global Moran’s I = 0.56 (***)
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The distribution of crime across New York City exhibits spatial auto-­correlation, so we opt
for a spatial econometrics model
Spatial Lag Model
C -­ crime counts in a census tract
A -­ census tract’s area
W -­ spatial weight matrix (Queen)
RP -­ variables describing the resident population
AP -­ variables describing the ambient population

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Control Area −0.12 (∗∗∗)
Population
Density &
Diversity
Population count +0.50 (∗∗∗)
Racial-­ethnic equitability index +0.14 (∗∗∗)
Income equitability index −0.10 (∗∗∗)
Neighborhood
Stability
Fraction vacant households +0.13 (∗∗∗)
Fraction rented households +0.55 (∗∗∗)
Fraction stable population −0.12 (∗∗∗)
Venues
Density &
Diversity
Venues +0.59 (∗∗∗)
Venues equitability index +0.25 (∗∗∗)
Checkins Checkins vs. population local quotiens +0.18 (∗∗∗)
Correlations of neighborhood variables and crime
Pearson correlation coefficient with the log-­transformed crime counts (independent variables Box-­Cox transformed & standardized)
8

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Control Area +0.02 (∗∗∗)
Population
Density &
Diversity
Population count +0.09 (∗∗∗)
Racial-­ethnic equitability index +0.01
Income equitability index −0.07 (∗∗∗)
Neighborhood
Stability
Fraction vacant households +0.04 (∗∗∗)
Fraction rented households +0.18 (∗∗∗)
Fraction stable population +0.05 (∗∗∗)
Venues
Density &
Diversity
Venues +0.46 (∗∗∗)
Venues equitability index -­0.04 (∗∗∗)
Checkins Checkins vs. population local quotiens -­0.19 (∗∗∗)
Spatial regression results
Spatial Lag Model results (independent variables Box-­Cox transformed and standardidized, dependent variable log-­transformed)
Model Pseudo R2
Census only 0.44
LBSN only 0.47
Census +
LBSN
0.56
9
Constant +3.82 (∗∗∗)
Spatial lag +0.31 (∗∗∗)

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In general, the sign and relative size of the coefficients stays similar across the the different
crime types, with some notable exceptions
The model performs best for grand larcenies, and worst for robberies
10
Negative coef.
Positive coef.

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Limitations and others things to think about
● NOT causality – observational study, not controlled experiment!
● Problem definition – dependent variable is aggregated over a long time period, which is not an actionable
insights for police patrolling, but more for urban planning
○ Mitigation: construct a spatio-­temporal prediction model for short-­term crime description/prediction
● Generalization – study analyses only data from one particular city
○ Mitigations: test on completely new data, compare and contrast different cities and countries
● Bias in the data – Foursquare data exhibit geographical and social biases
○ Mitigation: For now: NYC is the most active city on Foursquare, When moving to new geographies: bias needs to be quantified?
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Take-­aways
● The novel factors are significantly related to the long-­term crime levels in an area
● The novel factors and the geographical influence improve the baseline models based on census factors
● Support for Jacob’s Eyes on the Street theory and Brantingham^2 criminogenic places theory
12
+

13. www.im.ethz.ch/people/ckadar
@tweeting_cris
Thank you! Questions or remarks?

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Keep the variables count low – rely heavily on aggregate metrics!
(counts, equitability indexes, fractions, local quotients)
Equitability indexes
○ Intuition: lower values indicate the relative abundance of a given activity, while higher values indicate
equi-­probability of all activities (professional, food, resident, commuting, etc.)!
Local quotients
○ Intuition: neighborhoods with LQ >> 1 can be regarded as (digital) hot spots!
14

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Control Area +0.10 (∗∗∗)
Population
Density &
Diversity
Population count +0.24 (∗∗∗)
Racial-­ethnic equitability index +0.03 (∗∗∗)
Income equitability index −0.02
Neighborhood
Stability
Fraction vacant households +0.09 (∗∗∗)
Fraction rented households +0.20 (∗∗∗)
Fraction stable population −0.02
Spatial regression results – census only
Spatial Lag Model results (independent variables Box-­Cox transformed and standardidized, dependent variable log-­transformed)
Model Pseudo R2
Census only 0.44
15
Constant +2.50 (∗∗∗)
Spatial lag +0.55 (∗∗∗)

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Control Area +0.02 (∗∗∗)
Population
Density &
Diversity
Population count +0.09 (∗∗∗)
Racial-­ethnic equitability index +0.01
Income equitability index −0.07 (∗∗∗)
Neighborhood
Stability
Fraction vacant households +0.04 (∗∗∗)
Fraction rented households +0.18 (∗∗∗)
Fraction stable population +0.05 (∗∗∗)
Venues
Density &
Diversity
Venues +0.46 (∗∗∗)
Venues equitability index -­0.04 (∗∗∗)
Checkins Checkins vs. population local quotiens -­0.19 (∗∗∗)
Spatial regression results – all
Spatial Lag Model results (independent variables Box-­Cox transformed and standardidized, dependent variable log-­transformed)
Model Pseudo R2
Census +
LBSN
0.56
16
Constant +3.82 (∗∗∗)
Spatial lag +0.31 (∗∗∗)

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Control Area -­0.05 (∗∗∗)
Venues
Density &
Diversity
Venues +0.63 (∗∗∗)
Venues equitability index 0.00
Checkins Checkins vs. population local quotiens -­0.33 (∗∗∗)
Spatial regression results – LBSN only
Spatial Lag Model results (independent variables Box-­Cox transformed and standardidized, dependent variable log-­transformed)
Model Pseudo R2
LBSN only 0.47
17
Constant +3.79 (∗∗∗)
Spatial lag +0.32 (∗∗∗)