Identifying Gentrifying Tracts

In this exercise, you will identify and map the tracts that were gentrifying between 2000 and 2010. To be classified as gentifying, tracts must have been gentrifiable in 2000 and meet these criteria:

The percentage of the population with Bachelor's degrees or higher must be increasing faster than for the New York metro area.
House values must have increased since 2000. In order to account for inflation, house values from 2000 will be multiplied by 1.2612.

The GeoDataFrame bk_2010 has been loaded for you. The column names are shown in the console. Because you will be comparing 2010 with 2000, it contains data from both years, in columns suffixed with "_2000" and "_2010". It also has the column gentrifiable that you created in the last exercise.

This exercise is part of the course

Analyzing US Census Data in Python

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Exercise instructions

Set increasing_education to True if the increase in the percentage of population with Bachelor's degrees from 2000 to 2010 is greater than the MSA-level increase
Set increasing_house_value to True if the median_value_2010 is more than 1.2612 times greater than median_value_2000
Using the & operator, set gentrifying to True if a tract is gentrifiable and has increasing_education and has increasing_house_value
Map the gentrifying tracts using a "YlOrRd" colormap

Hands-on interactive exercise

Have a go at this exercise by completing this sample code.

# Increase in percent BA greater than MSA
bk_2010["increasing_education"] = ____

# Increase in house value
bk_2010["increasing_house_value"] = ____

# Identify gentryifying tracts
bk_2010["gentrifying"] = bk_2010["gentrifiable"] & ____

# Plot gentrifying tracts
bk_2010.plot(____)
plt.show()

Edit and Run Code