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Are High-Income Areas More Sensitive to Crime than Low-Income Areas?
Sources: www.istockphoto.com; www.dreamstime.com
A Spatial Analysis by Jacki Murdock
In the midterm we discovered that transit use in low income areas is
not significantly impacted by crime.
However, this may be because the site selected was transit dependent,
and therefore was not as sensitive to crime because they do not have
access to another mode of travel.
So, is transit use more affected by crime (i.e. more sensitive to crime) in
higher income areas compared with lower income areas?
I chose two sites based on the Los Angeles Times “mapping LA” site’s
median income data: Hancock Park and Watts.
Data: METRO bus ridership data for every month (September 2011-January 2012) LAPD crime data
for those months LA Times Mapping LA, ESRI, 2010 TIGER, MIT GIS Services
The Question
Hancock Park Watts
• 17,346 people per square mile
• 62% Latino
• 37% Black
• $25,161 Median Income
• 2.9% have a four year degree
• 48 Crimes in October, 2011
• 76 Bus Stops
• 6,459 people per square mile
•71% White
•13% Asian
• $85,277 Median Income
• 56.2% have a four year degree
• 25 Crimes in October, 2011
• 55 Bus Stops
Process of Site Selection
In order to determine if higher income, less transit dependent communities were more sensitive to crime than lower income, transit dependent communities, I chose a higher and lower income area based on the LATimes Crime Mapping rankings of neighborhoods.
Hancock Park
Watts
105 FreewayWilshire Boulevard
Total Ridership and Crime: W
atts
Tota
l Rid
ersh
ip a
nd C
rime:
Han
cock
Par
k
0 0.5 10.25 Miles
105 FreewayWilshire Boulevard
Mean Center: W
atts
Mea
n Ce
nter
: Han
cock
The Mean Center represents the shortest average
distance from crime or ridership. In Watts, the
average distance between crime and average distance
between bus riders is shorter than in Hancock Park .
0 0.5 10.25 Miles
105 FreewayWilshire Boulevard
Standard Distance: W
atts
Stan
dard
Dis
tanc
e: H
anco
ckThis circle represents one standard
deviation from the mean. 66% of crime and ridership happen within these circles.
So, the smaller the circle, the more clustered the crime/ridership. Much of
the ridership in Watts overlaps with most of the crime incidents, whereas most of
the crime/ridership does not intersect in Hancock Park
0 0.5 10.25 Miles
105 FreewayWilshire Boulevard
Standard Deviation Ellipse: W
atts
Stan
dard
Dev
iatio
n El
lipse
: Han
cock
The standard distance assumes a normal distribution of features within the circle. However, this is very rarely
the case. This measure shows the direction and the distribution of crime and ridership. In Hancock park, riders
are much less distributed than in Watts. Yet Crime seems to be similarly
distributed.
0 0.5 10.25 Miles
Ridership Crime0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
Clustered or Dispersed: Moran's Index
WattsHancock Park
Next, I ran a spatial autocorrelation test to see if the general pattern of features is clustered or dispersed.
In the Moran’s Index, the higher the value, the more clustered.Crime and Ridership are clustered in both Watts and Hancock Park
*All of the variables had statistically significant results at P-values below 0.05.
Wilshire Boulevard
Crime Incidents H
ot Spot: Hancock Park
Wilshire Boulevard
Tran
sit R
ider
ship
Hot
Spo
t: H
anco
ck P
ark
Wilshire Boulevard
To know more about why crime and
ridership are clustered, I performed
a Hot Spot Analysis
0 0.5 10.25 Miles
Crime Incidents H
ot Spot: Watt
s105 Freeway105 Freeway
Tran
sit R
ider
ship
Hot
Spo
t: W
atts
The Hot Spots, in red, are surrounded around high levels of ridership/crime. In addition, they
are surrounded around more ridership/crime than its neighbors.
This may be way Watts has no significant hot spots, because high
crime levels occur more dispersedly.
0 0.5 10.25 Miles
105 FreewayWilshire Boulevard
Average Distance to Crim
e and Ridership: Watt
sAv
erag
e D
istan
ce to
Crim
e an
d Ri
ders
hip:
Han
cock
Par
k Next, I used Network Analyst to
find the average distance to crime for each transit stop. Then, I ran
an OLS Regression to find out where stops proximity to crime
was effecting ridership.
105 FreewayWilshire Boulevard
Average Distance to Crim
e and Ridership: Watt
sAv
erag
e D
istan
ce to
Crim
e an
d Ri
ders
hip:
Han
cock
Par
k Next, I used Network Analyst to
find the average distance to crime for each transit stop. Then, I ran
an OLS Regression to find out where stops proximity to crime
was effecting ridership.
Watts Hancock Park0
0.001
0.002
0.003
0.004
0.005
0.006
0.007
R-Squared
R-Squared
R-Squared tells us how well the regression was able to predict crimes’ effect on transit use. In our case, the R-squared is extremely low in both cases, but is slightly higher in Watts, possibly due to its higher incidents of crime.
*All of the variables had statistically significant results at P-values below 0.05.
Implications
• We have seen that there are significant differences in sensitivity
to crime between a high-income and low-income area.
• Watts is not as sensitive to crime as is Hancock Park
• Presumably, this is because those transit stops where most
crime occurs in Hancock Park are not used as often because
their residents have access to vehicles, while many Watts’
residents do not.
Inset Map Graduated Symbols Aggregating Attribute Fields Attribute sub-set selections Geographic sub-set selections Geoprocessing Geocoding Charts Network Analyst (to find average distance to crime for each
transit stop) 5 Models Spatial Statistics (Mean Center, Standard Distance, Standard
Deviation Ellipse, Getis-Ord General Gi, Spatial Autocorrelation, OLS Regression)
Skills Used