Overlay dua layer poligon

Kembali ke himpunan data penggunaan lahan dan kecamatan, sekarang kita akan menggabungkan keduanya dalam operasi overlay. Buat GeoDataFrame baru yang berisi irisan poligon penggunaan lahan dengan masing-masing kecamatan, dan pastikan data atribut dari kedua layer sumber ikut terbawa.

GeoPandas sudah diimpor.

Latihan ini adalah bagian dari kursus

Bekerja dengan Data Geospasial di Python

Petunjuk latihan

Cetak baris pertama dari kedua himpunan data (land_use dan districts).
Buat GeoDataFrame baru dari irisan land_use dan districts. Simpan hasilnya ke variabel combined.
Cetak baris pertama dari GeoDataFrame hasil (combined).

Latihan interaktif praktis

Cobalah latihan ini dengan menyelesaikan kode contoh berikut.

# Print the first five rows of both datasets
print(____)
print(____)

# Overlay both datasets based on the intersection
combined = geopandas.____(____, ____, how='____')

# Print the first five rows of the result
print(____)

Edit dan Jalankan Kode

Latihan ini adalah bagian dari kursus

Bekerja dengan Data Geospasial di Python

SkillTag.level.intermediateSkillTag.label

4.8+

Mulai Kursus Gratis

In this chapter, you will be introduced to the concepts of geospatial data, and more specifically of vector data. You will then learn how to represent such data in Python using the GeoPandas library, and the basics to read, explore and visualize such data. And you will exercise all this with some datasets about the city of Paris.

Exercise 1: Geospatial data Exercise 2: Restaurants in Paris Exercise 3: Adding a background map Exercise 4: Introduction to GeoPandas Exercise 5: Explore the Paris districts (I)Exercise 6: Explore the Paris districts (II)Exercise 7: The Paris restaurants as a GeoDataFrame Exercise 8: Exploring and visualizing spatial data Exercise 9: Visualizing the population density Exercise 10: Using pandas functionality: groupby Exercise 11: Plotting multiple layers

One of the key aspects of geospatial data is how they relate to each other in space. In this chapter, you will learn the different spatial relationships, and how to use them in Python to query the data or to perform spatial joins. Finally, you will also learn in more detail about choropleth visualizations.

Exercise 1: Shapely geometries and spatial relationships Exercise 2: Creating a Point geometry Exercise 3: Shapely's spatial methods Exercise 4: Spatial relationships with GeoPandas Exercise 5: In which district in the Eiffel Tower located?Exercise 6: How far is the closest restaurant?Exercise 7: The spatial join operation Exercise 8: Paris: spatial join of districts and bike stations Exercise 9: Map of tree density by district (1)Exercise 10: Map of tree density by district (2)Exercise 11: Choropleths Exercise 12: Equal interval choropleth Exercise 13: Quantiles choropleth Exercise 14: Compare classification algorithms

In this chapter, we will take a deeper look into how the coordinates of the geometries are expressed based on their Coordinate Reference System (CRS). You will learn the importance of those reference systems and how to handle it in practice with GeoPandas. Further, you will also learn how to create new geometries based on the spatial relationships, which will allow you to overlay spatial datasets. And you will further practice this all with Paris datasets!

Exercise 1: Sistem Referensi Koordinat Exercise 2: Koordinat geografis vs koordinat proyeksi Exercise 3: Bekerja dengan sistem koordinat di GeoPandas Exercise 4: Memproyeksikan GeoDataFrame Exercise 5: Memproyeksikan Titik Exercise 6: Menghitung jarak dalam CRS terproyeksi Exercise 7: Memproyeksikan ke Web Mercator untuk menggunakan web tiles Exercise 8: Operasi spasial: membuat geometri baru Exercise 9: Mengeksplorasi himpunan data Penggunaan Lahan Exercise 10: Irisan dua poligon Exercise 11: Mengiris GeoDataFrame dengan sebuah Poligon Exercise 12: Melapiskan himpunan data spasial Exercise 13: Overlay dua layer poligon

Latihan Saat Ini

Exercise 14: Menginspeksi hasil overlay

In this final chapter, we leave the Paris data behind us, and apply everything we have learnt up to now on a brand new dataset about artisanal mining sites in Eastern Congo. Further, you will still learn some new spatial operations, how to apply custom spatial operations, and you will get a sneak preview into raster data.

Exercise 1: Introduction to the dataset Exercise 2: Import and explore the data Exercise 3: Convert to common CRS and save to a file Exercise 4: Styling a multi-layered plot Exercise 5: Additional spatial operations Exercise 6: Buffer around a point Exercise 7: Mining sites within national parks Exercise 8: Applying custom spatial operations Exercise 9: Finding the name of the closest National Park Exercise 10: Applying a custom operation to each geometry Exercise 11: Working with raster data Exercise 12: Import and plot raster data Exercise 13: Extract information from raster layer Exercise 14: The end