Back to The Socio-Economics of Crime; Burglary in Washington, D.C.

Proximity to Major Roads and Per Capita Income Analysis

• Created a raster named “PCI” of the census tract shapefile based on the “per capita income” (PCI) field. 
• Reclassified the “PCI” raster: all cells with a value of 35,000 and higher were given a value of “1,” while all cells with a value of less than 35,000 were assigned a value of “0” and named the resulting raster “PCI Reclass.” 
• Created a straight-line distance raster of 153 meters (~500ft) from all major roads named “Major Roads Proximity,” and reclassified all values to a value of “1.”

 To find what percentage of the city’s total area was composed of areas that were within 500 feet of a major road (i.e. the total area of land within 500 feet of a major road divided by total area of the city):

• Multiplied the amount of cells in the “Major Roads Proximity” raster by the area represented by each cell (100 m˛).
• Divided by the product of the number of cells in the original “PCI” raster multiplied by the area represented by a cell (100 m˛). 

 75,224,700 m˛/ 176,989,300 m˛ = 42%

 To find what percentage of the areas within 50 meters of a burglary are also within 500 feet of a major road:

• Used the Raster Calculator to multiply the “Major Roads Proximity” raster by the “Burglary Proximity Zones” raster. 
• Multiplied the number of cells with a value of “1” in the resulting raster by an individual cell’s area (100 m˛).
• Divided by the product of the total number of cells of the “Burglary Proximity Zone” raster multiplied by the area represented by a cell (100 m˛).

 8,796,000 m˛ / 15,777,300 m˛ = 56%

Thus, 56% of the cumulative Burglary Proximity Zones area (areas within 50 m of a burglary) is located within 500 feet of major road, despite the fact that areas within 500 feet of a major road make up only 42% of the city’s total area.

To find if the relationship between burglaries and major roads is affected by wealth: 

• Within Raster Calculator: “Burglary Proximity Zone” * “PCI Reclass”

The result is a raster where all cells with a value of “1” represent areas that are within 50 meters of a burglary and in a census tract with a PCI equal to or greater than $35,000.  The total area is 4,124,000 m˛.

Cells with a value of “0” represent areas that are within 50 meters of a burglary and in a census tract with a PCI less than $35,000.  The total area is 11,425,200 m˛.

• Within Raster Calculator: “Burglary Proximity Zone” * “PCI Reclass” * “Major Roads Proximity”

 The result is a raster where all cells with a value of “1” represent areas that are within 50 meters of a burglary, within 500 feet of a major road, and in a census tract with a PCI equal to or greater than $35,000.  The total area is 2,705,200 m˛.  Cells with a value of “0” represent areas that are within 50 meters of a burglary, within 500 feet of a major road, and in a census tract with a PCI less than $35,000.  The total area is 6,076,600 m˛.

 2,705,200 m˛/ 4,124,000 m˛ = 66%

66% of the Burglary Proximity Zone located in tracts with a per capita income of $35,000 or greater is also within 500 feet of a major road.

 6,076,600 m˛/ 11,425,200 m˛ = 53%

53% of the Burglary Proximity Zone located in tracts with a per capita income of less than $35,000 is also within 500 feet of a major road.

Thus, it seems that proximity to a major road is more significant to burglary density in wealthy neighborhoods than in less wealthy and poor neighborhoods.

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Population Density Analysis

• Created a raster of the census tracts shapefile based on the population density field and named it “PopDensity.”
• Reclassified “PopDensity” raster so that all cells with a value of .005 or greater (5,000 persons/k m˛) are given a value of “1” and all other cells are “0” and named it “PopDensity_Reclass”

 To find what percentage of the city’s total area is comprised of areas where the census tract has a population density of at least 5,000 persons/km˛:

• Divided the number of cells in “PopDensity_Reclass” with a value of “1” by the total number of cells.

340,775 / 1,769,893 * 100 = 19%

To find what percentage of the Burglary Proximity Zones’ total area is located in census tracts where the population density is at least 5,000 persons/km˛:

• In Raster Calculator: “Burglary Proximity Zones” raster * “PopDensity_Reclass” raster

The resulting raster has values of “1” where the cells are within 50 meters of a burglary (i.e. a burglary Proximity Zone) and is also in a census tract where the  population density is at least 5,000 persons/km˛.

• Divided the total number of cells in the raster result with a value of “1” by the total number of cells in the “Burglary Proximity Zones” raster.

 66,701 / 155,773 * 100 = 43%

Thus, even though areas where the population density is 5,000 persons/km˛ or greater make up only 19% of the city, 43% of the Burglary Proximity Zones' cumulative area is located there.

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Grandparents as Primary Caregivers Analysis

• Created a raster of the census tracts shapefile based on the field that shows the proportion of adults 30 years or older who are responsible for grandchildren and named it “Grandkids.”
• Reclassified “Grandkids” raster so that all cells with a value of .07 or greater (7% of all adults of 30 years old) are given a value of “1” and all other cells are “0” and named it “Grandkids_Reclass.”

To find what percentage of the city’s total area is comprised of areas where the census tract has a percentage of adults over 30 who are responsible for grandchildren equal to or greater than 7%:

• Divided the number of cells in “Grandkids_Reclass” with a value of “1” by the total number of cells.

488,586 / 1,769,893 * 100 = 28%

 To find what percentage of the Burglary Proximity Zones’ total area is located in census tracts where the percentage of adults over 30 who are responsible for grandchildren equal to or greater than 7%:

• In Raster Calculator: “Burglary Proximity Zones” raster * “Grandkids_Reclass” raster

The resulting raster has values of “1” where the cells are within 50 meters of a burglary (i.e. a Burglary Proximity Zone) and is also in a census tract where the  percentage of adults over 30 who are responsible for grandchildren equal to or greater than 7%.

• Divided the total number of cells in the raster result with a value of “1” by the total number of cells in the “Burglary Proximity Zones” raster.

66,255 / 155,773 * 100 = 43%

Thus, even though areas where the  percentage of adults over 30 who are responsible for grandchildren is equal to or greater than 7% make up only 28% of the city, 43% of the Burglary Proximity Zones' cumulative area is located there.

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Poverty Analysis

• Created a raster of the census tracts shapefile based on the field that shows what percentage of people in a census tract are below poverty and named it “Poverty.”
• Reclassified “Poverty” raster so that all cells with a value of .18 or greater (18% of the population in the census tract  is below poverty) are given a value of “1” and all other cells are “0” and named it “Poverty_Reclass”

To find what percentage of the city’s total area is comprised of areas where the census tract has a percentage of people below poverty of at least 18%:

• Divided the number of cells in “Poverty_Reclass” with a value of “1” by the total number of cells.

662,273 / 1,769,893 * 100 = 37%

To find what percentage of the Burglary Proximity Zones’ total area is located in census tracts where the percentage of people below poverty is at least 18%:

• In Raster Calculator: “Burglary Proximity Zones” raster * “Poverty_Reclass” raster

The resulting raster has values of “1” where the cells are within 50 meters of a burglary (i.e. a burglary Proximity Zone) and is also in a census tract where the percentage of people below poverty is at least 18%.

• Divided the total number of cells in the raster result with a value of “1” by the total number of cells in the “Burglary Proximity Zones” raster.

83,688 / 155,773 * 100 = 54%

Thus, even though areas where the percentage of people below poverty is 18% or greater make up only 37% of the city, 54% of the Burglary Proximity Zones' cumulative area is located there.

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Educational Attainment of Males

• Created a raster of the census tracts shapefile based on the field that shows what percentage of males at least 25 years old do not have a high school degree or equivalency and named it “Education.”
• Reclassified “Education” raster so that all cells with a value of .27 or greater (within a census tract where 27% or more of males 25 and older don’t have a high school degree or equivalency) are given a value of “1” and all other cells are “0” and named it “Education_Reclass”

To find what percentage of the city’s total area is comprised of areas where the census tract has a percentage of males at least 25 years old who don’t have a high school degree or equivalency of at least 27%:

• Divided the number of cells in “Education _Reclass” with a value of “1” by the total number of cells.

636,432 / 1,769,893 * 100 = 36%

To find what percentage of the Burglary Proximity Zones’ total area is located in census tracts where the percentage of people below poverty is at least 18%:

• In Raster Calculator: “Burglary Proximity Zones” raster * “Poverty_Reclass” raster

The resulting raster has values of “1” where the cells are within 50 meters of a burglary (i.e. a burglary Proximity Zone) and is also in a census tract where at least 27% of males at least 25 years old do not have a high school degree or equivalency.

• Divided the total number of cells in the raster result with a value of “1” by the total number of cells in the “Burglary Proximity Zones” raster.

83,688 / 155,773 * 100 = 54%

Thus, even though areas where 27% (or more) of all males 25 or older lack a high school degree or equivalency make up 36% of the city, 54% of the Burglary Proximity Zones are located in these tracts.

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