Summary  
This chapter demonstrates how to use GeoPandas to load and inspect vector spatial data, visualize polygon boundaries, and calculate spatial attributes such as area and density.  

General domain of usage  
Urban area analysis

This video presents a case study involving a real urban area dataset. You will be introduced to the dataset's structure, the main questions guiding the analysis, and the workflow for exploring urban boundaries, land use, and population trends using Python geospatial tools. The video demonstrates the importance of spatial data in uncovering urban patterns and sets the stage for hands-on analysis.

Urban areas are dynamic landscapes shaped by population growth, infrastructure, and changing land use. As you explore urban spatial data, you will often start by examining the boundaries of a city or metropolitan region. Understanding these boundaries helps you contextualize patterns such as **density**, **accessibility**, and **neighborhood structure**. With Python and the `geopandas` library, you can easily load, inspect, and visualize urban area boundaries, which is a foundational step in geospatial analysis.

import geopandas as gpd
import matplotlib.pyplot as plt

# Load urban area boundaries from a GeoJSON file (example URL)
url = "https://raw.githubusercontent.com/datasets/geo-boundaries-world-110m/master/countries.geojson"
urban_areas = gpd.read_file(url)

# Inspect the first few records and their attributes
print(urban_areas.head())

# Plot the urban area boundaries
fig, ax = plt.subplots(figsize=(10, 6))
urban_areas.plot(ax=ax, edgecolor="black", facecolor="lightgray")
ax.set_title("Urban Area Boundaries")
plt.show()

Once you have loaded and visualized the urban area boundaries, you can move on to analyzing their spatial attributes. Key statistics such as **total area** and **population density** reveal much about the structure and challenges of urban environments. By leveraging the spatial and tabular capabilities of `geopandas`, you can efficiently calculate these statistics and summarize the results for further interpretation.


import unittest
import user_code
import ast
import re   
import importlib
import csv
import unittest
import importlib
import sys
import io
from contextlib import redirect_stdout

class TestTask(unittest.TestCase):
    def setUp(self):
        self.url = "https://raw.githubusercontent.com/datasets/geo-boundaries-world-110m/master/countries.geojson"
        self.population_column = "POP_EST"

    def test_loads_geospatial_data(self):
        import user_code
        importlib.reload(user_code)
        # Patch print to capture output
        f = io.StringIO()
        with redirect_stdout(f):
            user_code.summarize_urban_area_stats(self.url, population_column=None)
        output = f.getvalue()
        _dynamic_test(
            self,
            "area_sqkm" in output,
            "Function prints summary with area_sqkm column.",
            f"Expected 'area_sqkm' in printed output, got: {output}"
        )

    def test_area_sqkm_calculation(self):
        import user_code
        importlib.reload(user_code)
        # Patch print to capture output
        f = io.StringIO()
        with redirect_stdout(f):
            user_code.summarize_urban_area_stats(self.url, population_column=None)
        output = f.getvalue()
        # Check that area_sqkm values are present and numeric
        found_numeric = False
        for line in output.splitlines():
            if "area_sqkm" in line:
                continue
            fields = line.split()
            if len(fields) >= 2:
                try:
                    val = float(fields[1])
                    found_numeric = True
                except Exception:
                    continue
        _dynamic_test(
            self,
            found_numeric,
            "Area in square kilometers is calculated and printed.",
            "Area values are not numeric or missing from output."
        )

    def test_population_density_calculation(self):
        import user_code
        importlib.reload(user_code)
        f = io.StringIO()
        with redirect_stdout(f):
            user_code.summarize_urban_area_stats(self.url, population_column=self.population_column)
        output = f.getvalue()
        # Look for population_density column header and at least one numeric value below it
        found_density_header = "population_density" in output
        found_numeric_density = False
        for line in output.splitlines():
            if "population_density" in line:
                continue
            fields = line.split()
            if len(fields) >= 3:
                try:
                    val = float(fields[2])
                    found_numeric_density = True
                except Exception:
                    continue
        _dynamic_test(
            self,
            found_density_header and found_numeric_density,
            "Population density column is printed with numeric values.",
            "Population density column or its values are missing or not numeric."
        )

    def test_summary_first_three_areas(self):
        import user_code
        importlib.reload(user_code)
        f = io.StringIO()
        with redirect_stdout(f):
            user_code.summarize_urban_area_stats(self.url, population_column=self.population_column)
        output = f.getvalue()
        # Count the number of data lines (skip header)
        lines = [l for l in output.splitlines() if l.strip() and not l.startswith("  name") and not l.startswith("name")]
        # Remove header line(s)
        if lines and ("area_sqkm" in lines[0] or "population_density" in lines[0]):
            lines = lines[1:]
        _dynamic_test(
            self,
            2 <= len(lines) <= 3,
            "Function prints summary for first three urban areas.",
            f"Expected 2 or 3 summary rows, got {len(lines)}."
        )

def _dynamic_test(test_case, condition, success_message, failure_message):
    if condition:
        test_case._testMethodName = success_message
        test_case.assertTrue(True, success_message)
    else:
        test_case._testMethodName = failure_message
        test_case.fail(failure_message)

def normalize_text(text):
    text = text.lower()
    text = re.sub(r"\\s{2,}", " ", text)
    text = re.sub(r"\\s*([,:?])\\s*", r"\\1 ", text)
    return text.strip()

def change_var(code: str, var_name: str, value: str) -> str:
    tree = ast.parse(code)
    lines = code.splitlines()
    changed = False
    # Collect all assignment nodes to modify
    assign_nodes = [
        (i, node)
        for i, node in enumerate(tree.body)
        if isinstance(node, ast.Assign)
        and any(isinstance(target, ast.Name) and target.id == var_name for target in node.targets)
    ]

    # If nothing to change, return unmodified code
    if not assign_nodes:
        return code

    # Perform replacements for all matching assignments (from last to first to not break line offsets)
    for i, node in reversed(assign_nodes):
        start_line = node.lineno - 1
        line = lines[start_line]
        indent = ' ' * (len(line) - len(line.lstrip()))
        lines[start_line] = f"{indent}{var_name} = {value}"
        next_line = len(lines)
        for next_node in tree.body[i+1:]:
            if hasattr(next_node, 'lineno'):
                next_line = next_node.lineno - 1
                break
        if next_line > start_line + 1:
            lines[start_line+1:next_line] = []
        changed = True

    return '\\n'.join(lines) if changed else code

if __name__ == "__main__":
    unittest.main()


test_main.py

Dive into the fundamentals of geospatial data analysis using Python. This intermediate course introduces you to the core concepts and practical applications of geospatial analysis, focusing on real-world datasets and hands-on projects. You'll learn to manipulate, visualize, and analyze spatial data using Python and popular libraries, with a special emphasis on geopandas. The course is designed to be engaging, featuring interactive grids, videos, and quizzes to reinforce your learning.

Explore the essentials of geospatial data, coordinate systems, and the essential tools for spatial analysis in Python. This section sets the foundation for working with spatial datasets and introduces geopandas in detail, including its core data structures, reading/writing capabilities, and integration with pandas and matplotlib.

Delve into core spatial analysis techniques, including spatial joins, overlays, and aggregations, using geopandas and visualization libraries. This section covers advanced geopandas methods for combining, comparing, and summarizing spatial datasets, as well as best practices for spatial data visualization.

Gain a solid understanding of real-world geospatial datasets and scenarios, focusing on practical analysis and visualization challenges. This section emphasizes project-based learning and synthesis of geospatial analysis techniques.

Urban Area Analysis

Solution