Paris: penggabungan spasial distrik dan stasiun sepeda

Mari kembali ke data Paris tentang distrik dan stasiun sepeda. Sekarang kita akan menggunakan operasi penggabungan spasial untuk mengidentifikasi distrik tempat setiap stasiun berada.

Himpunan data distrik dan stasiun bike sharing sudah dimuat untuk Anda sebagai GeoDataFrame districts dan stations, dan GeoPandas telah diimpor sebagai geopandas

Latihan ini adalah bagian dari kursus

Bekerja dengan Data Geospasial di Python

Petunjuk latihan

Gunakan fungsi geopandas.sjoin() untuk menambahkan nama distrik tempat setiap stasiun berada ke himpunan data stations. Simpan hasilnya sebagai joined.
Periksa hasilnya dengan menampilkan lima baris pertama.

Latihan interaktif praktis

Cobalah latihan ini dengan menyelesaikan kode contoh berikut.

# Join the districts and stations datasets
joined = ____

# Inspect the first five rows of the result
print(joined.____)

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: Geometri Shapely dan relasi spasial Exercise 2: Membuat geometri Point Exercise 3: Metode spasial Shapely Exercise 4: Relasi spasial dengan GeoPandas Exercise 5: Di distrik mana Menara Eiffel berada?Exercise 6: Seberapa jauh restoran terdekat?Exercise 7: Operasi penggabungan spasial Exercise 8: Paris: penggabungan spasial distrik dan stasiun sepeda

Latihan Saat Ini

Exercise 9: Peta kepadatan pohon per distrik (1)Exercise 10: Peta kepadatan pohon per distrik (2)Exercise 11: Kloroplet Exercise 12: Choropleth interval sama Exercise 13: Choropleth kuantil Exercise 14: Bandingkan algoritma klasifikasi

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: Coordinate Reference Systems Exercise 2: Geographic vs projected coordinates Exercise 3: Working with coordinate systems in GeoPandas Exercise 4: Projecting a GeoDataFrame Exercise 5: Projecting a Point Exercise 6: Calculating distance in a projected CRS Exercise 7: Projecting to Web Mercator for using web tiles Exercise 8: Spatial operations: creating new geometries Exercise 9: Exploring a Land Use dataset Exercise 10: Intersection of two polygons Exercise 11: Intersecting a GeoDataFrame with a Polygon Exercise 12: Overlaying spatial datasets Exercise 13: Overlay of two polygon layers Exercise 14: Inspecting the overlay result

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