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Are High-Income Areas More Sensitive to Crime than Low-Income Areas?. A Spatial Analysis by Jacki Murdock. Sources: www.istockphoto.com; www.dreamstime.com. In the midterm we discovered that transit use in low income areas is not significantly impacted by crime .

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Are High-Income Areas More Sensitive to Crime than Low-Income Areas?

A Spatial Analysis by Jacki Murdock

Sources: www.istockphoto.com; www.dreamstime.com


  • 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

  • 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

  • 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


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




Total Ridership and Crime: Watts

Total Ridership and Crime: Hancock Park

105 Freeway

Wilshire Boulevard


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 .

Mean Center: Watts

Mean Center: Hancock

105 Freeway

Wilshire Boulevard


This 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

Standard Distance: Watts

Standard Distance: Hancock

105 Freeway

Wilshire Boulevard


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.

Standard Deviation Ellipse: Watts

Standard Deviation Ellipse: Hancock

105 Freeway

Wilshire Boulevard


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.


To know more about why crime and ridership are clustered, I performed a Hot Spot Analysis

Crime Incidents Hot Spot: Hancock Park

Transit Ridership Hot Spot: Hancock Park

Wilshire Boulevard

Wilshire Boulevard

Wilshire Boulevard


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.

Crime Incidents Hot Spot: Watts

Transit Ridership Hot Spot: Watts

105 Freeway

105 Freeway


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.

Average Distance to Crime and Ridership: Watts

Average Distance to Crime and Ridership: Hancock Park

Wilshire Boulevard

105 Freeway


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.

Average Distance to Crime and Ridership: Watts

Average Distance to Crime and Ridership: Hancock Park

Wilshire Boulevard

105 Freeway


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
Implications predict

  • 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 predict

  • 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


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