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Data reclassification

Reclassifying data based on specific criteria is a common task when doing GIS analysis. The purpose of this lesson is to see how we can reclassify values based on some criteria which can be whatever, such as:

1. if available space in a pub is less than the space in my wardrobe

AND

2. the temperature outside is warmer than my beer

------------------------------------------------------

IF TRUE: ==> I go and drink my beer outside
IF NOT TRUE: ==> I go and enjoy my beer inside at a table

Even though, the above would be an interesting study case, we will use slightly more traditional cases to learn classifications. We will use Corine land cover layer from year 2012, and a Travel Time Matrix data from Helsinki to classify some features of them based on our own self-made classifier, or using a ready made classifiers that are commonly used e.g. when doing visualizations.

The target in this part of the lesson is to:

  1. classify the lakes into big and small lakes where

    • a big lake is a lake that is larger than the average size of all lakes in our study region
    • a small lake ^ vice versa
  2. use travel times and distances to find out
    • good locations to buy an apartment with good public tranportation accessibility to city center
    • but from a bit further away from city center where the prices are lower (or at least we assume so).
  3. use ready made classifiers from pysal -module to classify travel times into multiple classes.

Download data

Download (and then extract) the dataset zip-package used during this lesson from this link.

You should have following Shapefiles in the data folder:

$ cd /home/geo/L4/data
$ ls
Corine2012_Uusimaa.cpg      Helsinki_borders.cpg                       TravelTimes_to_5975375_RailwayStation.dbf
Corine2012_Uusimaa.dbf      Helsinki_borders.dbf                       TravelTimes_to_5975375_RailwayStation.prj
Corine2012_Uusimaa.prj      Helsinki_borders.prj                       TravelTimes_to_5975375_RailwayStation.shp
Corine2012_Uusimaa.shp      Helsinki_borders.shp                       TravelTimes_to_5975375_RailwayStation.shx
Corine2012_Uusimaa.shp.xml  Helsinki_borders.shx
Corine2012_Uusimaa.shx      TravelTimes_to_5975375_RailwayStation.cpg

Data preparation

Before doing any classification, we need to prepare our data a little bit.

Let's read the data in and select only English columns from it and plot our data so that we can see how it looks like on a map.

python

import gdal import geopandas as gpd import matplotlib.pyplot as plt import os

fp = os.path.join(os.path.abspath('data'), "Corine2012_Uusimaa.shp") data = gpd.read_file(fp)

import geopandas as gpd
import matplotlib.pyplot as plt

# File path
fp = "/home/data/Corine2012_Uusimaa.shp"

data = gpd.read_file(fp)

Let's see what we have.

python

data.head(2)

Let's select only English columns

python

# Select only English columns selected_cols = ['Level1', 'Level1Eng', 'Level2', 'Level2Eng', 'Level3', 'Level3Eng', 'Luokka3', 'geometry']

# Select data data = data[selected_cols]

# What are the columns now? data.columns

Let's plot the data and use column 'Level3' as our color.

python

data.plot(column='Level3', linewidth=0.05)

# Use tight layout and remove empty whitespace around our map @savefig corine-level3.png width=7in plt.tight_layout()

Let's see what kind of values we have in 'Level3Eng' column.

python

list(data['Level3Eng'].unique())

Okey we have plenty of different kind of land covers in our data. Let's select only lakes from our data. Selecting specific rows from a DataFrame based on some value(s) is easy to do in Pandas / Geopandas using a specific indexer called .ix[], read more from here..

python

# Select lakes (i.e. 'waterbodies' in the data) and make a proper copy out of our data lakes = data.ix[data['Level3Eng'] == 'Water bodies'].copy() lakes.head(2)

Calculations in DataFrames

Okey now we have our lakes dataset ready. The aim was to classify those lakes into small and big lakes based on the average size of all lakes in our study area. Thus, we need to calculate the average size of our lakes.

Let's check the coordinate system.

python

# Check coordinate system information data.crs

Okey we can see that the units are in meters and we have a UTM projection.

Let's calculate first the are of our lakes.

python

# Calculate the area of lakes lakes['area'] = lakes.area

# What do we have? lakes['area'].head(2)

Notice that the values are now in square meters.. Let's change those into square kilometers so they are easier to read. Doing calculations in Pandas / Geopandas are easy to do:

python

lakes['area_km2'] = lakes['area'] / 1000000

# What is the mean size of our lakes? l_mean_size = lakes['area_km2'].mean() l_mean_size

Okey so the size of our lakes seem to be approximately 1.58 square kilometers.

Note

It is also easy to calculate e.g. sum or difference between two or more layers (plus all other mathematical operations), e.g.:

# Sum two columns
data['sum_of_columns'] = data['col_1'] + data['col_2']

# Calculate the difference of three columns
data['difference'] = data['some_column'] - data['col_1'] + data['col_2']

Classifying data

Creating a custom classifier

Let's create a function where we classify the geometries into two classes based on a given threshold -parameter. If the area of a polygon is lower than the threshold value (average size of the lake), the output column will get a value 0, if it is larger, it will get a value 1. This kind of classification is often called a binary classification.

First we need to create a function for our classification task. This function takes a single row of the GeoDataFrame as input, plus few other parameters that we can use.

def binaryClassifier(row, source_col, output_col, threshold):
    # If area of input geometry is lower that the threshold value
    if row[source_col] < threshold:
        # Update the output column with value 0
        row[output_col] = 0
    # If area of input geometry is higher than the threshold value update with value 1
    else:
        row[output_col] = 1
    # Return the updated row
    return row

python

def binaryClassifier(row, source_col, output_col, threshold):

# If area of input geometry is lower that the threshold value if row[source_col] < threshold: # Update the output column with value 0 row[output_col] = 0 # If area of input geometry is higher than the threshold value update with value 1 else: row[output_col] = 1 # Return the updated row return row

Let's create an empty column for our classification

python

lakes['small_big'] = None

We can use our custom function by using a Pandas / Geopandas function called .apply(). Thus, let's apply our function and do the classification.

python

lakes = lakes.apply(binaryClassifier, source_col='area_km2', output_col='small_big', threshold=l_mean_size, axis=1)

Let's plot these lakes and see how they look like.

python

lakes.plot(column='small_big', linewidth=0.05, cmap="seismic")

@savefig small-big-lakes.png width=6in plt.tight_layout()

Okey so it looks like they are correctly classified, good. As a final step let's save the lakes as a file to disk.

In [20]: outfp_lakes = r"/home/geo/lakes.shp"
In [21]: lakes.to_file(outfp_lakes)

python

outfp_lakes = os.path.join(os.path.abspath('data'), "lakes.shp") lakes.to_file(outfp_lakes)

Note

There is also a way of doing this without a function but with the previous example might be easier to understand how the function works. Doing more complicated set of criteria should definitely be done in a function as it is much more human readable.

Let's give a value 0 for small lakes and value 1 for big lakes by using an alternative technique:

lakes['small_big_alt'] = None
lakes.loc[lakes['area_km2'] < l_mean_size, 'small_big_alt'] = 0
lakes.loc[lakes['area_km2'] >= l_mean_size, 'small_big_alt'] = 1

Multicriteria data classification

It also possible to do classifiers with multiple criteria easily in Pandas/Geopandas by extending the example that we started earlier. Now we will modify our binaryClassifier function a bit so that it classifies the data based on two columns.

Let's call it customClassifier2 as it takes into account two criteria:

def customClassifier2(row, src_col1, src_col2, threshold1, threshold2, output_col):
    # 1. If the value in src_col1 is LOWER than the threshold1 value
    # 2. AND the value in src_col2 is HIGHER than the threshold2 value, give value 1, otherwise give 0
    if row[src_col1] < threshold1 and row[src_col2] > threshold2:
        # Update the output column with value 0
        row[output_col] = 1
    # If area of input geometry is higher than the threshold value update with value 1
    else:
        row[output_col] = 0

    # Return the updated row
    return row

python

def customClassifier2(row, src_col1, src_col2, threshold1, threshold2, output_col):
if row[src_col1] < threshold1 and row[src_col2] > threshold2:

row[output_col] = 1

else:

row[output_col] = 0

return row

Okey, now we have our classifier ready, let's use it to our data.

First, we need to read our Travel Time data from Helsinki into memory from the GeoJSON file that we prepared earlier with overlay analysis.

fp = r"/home/geo/TravelTimes_to_5975375_RailwayStation_Helsinki.geojson"

# Read the GeoJSON file similarly as Shapefile
acc = gpd.read_file(fp)

# Let's see what we have
acc.head(2)

python

import gdal import geopandas as gpd import os fp = os.path.join(os.path.abspath('data'), "TravelTimes_to_5975375_RailwayStation_Helsinki.geojson") acc = gpd.read_file(fp) acc.head(2)

Okey we have plenty of different variables (see from here the description for all attributes) but what we are interested in are columns called pt_r_tt which is telling the time in minutes that it takes to reach city center from different parts of the city, and walk_d that tells the network distance by roads to reach city center from different parts of the city (almost equal to Euclidian distance).

The NoData values are presented with value -1. Thus we need to remove those first.

python

acc = acc.ix[acc['pt_r_tt'] >=0]

Let's plot it and see how our data looks like.

python

import matplotlib.pyplot as plt

# Plot using 9 classes and classify the values using "Fisher Jenks" classification acc.plot(column="pt_r_tt", scheme="Fisher_Jenks", k=9, cmap="RdYlBu", linewidth=0);

# Use tight layour @savefig pt_time.png width=7in plt.tight_layout()

Okey so from this figure we can see that the travel times are lower in the south where the city center is located but there are some areas of "good" accessibility also in some other areas (where the color is red).

Let's also make a plot about walking distances

python

acc.plot(column="walk_d", scheme="Fisher_Jenks", k=9, cmap="RdYlBu", linewidth=0);

# Use tight layour @savefig walk_distances.png width=7in plt.tight_layout();

Okey, from here we can see that the walking distances (along road network) reminds more or less Euclidian distances.

Let's finally do our classification based on two criteria and find out grid cells where the travel time is lower or equal to 20 minutes but they are further away than 4 km (4000 meters) from city center.

Let's create an empty column for our classification results called "Suitable_area".

python

acc["Suitable_area"] = None

Now we are ready to apply our custom classifier to our data with our own criteria.

python

acc = acc.apply(customClassifier2, src_col1='pt_r_tt', src_col2='walk_d', threshold1=20, threshold2=4000, output_col="Suitable_area", axis=1)

Let's see what we got.

python

acc.head()

Okey we have new values in Suitable_area .column.

How many Polygons are suitable for us? Let's find out by using a Pandas function called value_counts() that return the count of different values in our column.

python

acc['Suitable_area'].value_counts()

Okey so there seems to be nine suitable locations for us where we can try to find an appartment to buy Let's see where they are located.

python

# Plot acc.plot(column="Suitable_area", linewidth=0);

# Use tight layour @savefig suitable_areas.png width=7in plt.tight_layout();

A-haa, okey so we can see that suitable places for us with our criteria seem to be located in the eastern part from the city center. Actually, those locations are along the metro line which makes them good locations in terms of travel time to city center since metro is really fast travel mode.

Task:

Try to change your classification criteria and see how your results change! What places would be suitable for you to buy an apartment in Helsinki region? You can also change the travel mode and see how they change the results.

Classification based on common classifiers

Pysal -module is an extensive Python library including various functions and tools to do spatial data analysis. It also includes all of the most common data classifiers that are used commonly e.g. when visualizing data. Available map classifiers in pysal -module are (see here for more details):

  • Box_Plot
  • Equal_Interval
  • Fisher_Jenks
  • Fisher_Jenks_Sampled
  • HeadTail_Breaks
  • Jenks_Caspall
  • Jenks_Caspall_Forced
  • Jenks_Caspall_Sampled
  • Max_P_Classifier
  • Maximum_Breaks
  • Natural_Breaks
  • Quantiles
  • Percentiles
  • Std_Mean
  • User_Defined

Let's apply one of those classifiers into our data and classify the travel times by public transport into 9 classes.

python

import pysal as ps

# Define the number of classes n_classes = 9

The classifier needs to be initialized first with make() function that takes the number of desired classes as input parameter.

python

# Create a Natural Breaks classifier classifier = ps.Natural_Breaks.make(k=n_classes)

Now we can apply that classifier into our data quite similarly as in our previous examples.

python

# Classify the data classifications = acc[['pt_r_tt']].apply(classifier)

# Let's see what we have classifications.head()

Okey, so we have a DataFrame where our input column was classified into 9 different classes (numbers 1-9) based on Natural Breaks classification.

Now we want to join that reclassification into our original data but let's first rename the column so that we recognize it later on.

python

# Rename the column so that we know that it was classified with natural breaks classifications.columns = ['nb_pt_r_tt']

# Join with our original data (here index is the key acc = acc.join(classifications)

# Let's see how our data looks like acc.head()

Great, now we have those values in our accessibility GeoDataFrame. Let's visualize the results and see how they look.

python

# Plot acc.plot(column="nb_pt_r_tt", linewidth=0, legend=True);

# Use tight layour @savefig natural_breaks_pt_accessibility.png width=7in plt.tight_layout()

And here we go, now we have a map where we have used one of the common classifiers to classify our data into 9 classes.