An warware: Geodata gani

Hannun Geodata kayan aiki ne mai ƙarfi wanda ke ba mu damar fahimtar hadaddun tsari da alaƙa tsakanin yanki da sauran bayanai. Yana taimakawa wajen yanke shawara mai fa'ida da gabatar da bayanai ta hanya mafi sauƙi kuma mai jan hankali. A cikin wannan labarin, za mu bincika yadda za a iya samun hangen nesa ta geodata ta amfani da Python, ɗaya daga cikin yarukan shirye-shirye da suka fi dacewa a yau. Za mu bincika ɗakunan karatu daban-daban, ayyuka, da dabarun da ake amfani da su don magance matsalolin gama-gari a wannan yanki, tabbatar da samun ingantaccen tushe don ginawa.

Gabatar da Geodata Visualization a Python

Python yana ba da ɗakunan karatu da yawa waɗanda aka tsara musamman don ganin geodata. Wasu daga cikin shahararrun wadanda suka hada da GeoPandas, Folium, Da kuma Makirci. Kowane ɗakin karatu yana aiki da manufarsa ta musamman, yana samar da ayyuka waɗanda za a iya amfani da su don ƙirƙirar taswirori masu ƙarfi da mu'amala, sigogi, da filaye masu alaƙa da geodata. A matsayin mai haɓakawa kuma ƙwararre a Python, yana da mahimmanci don fahimtar waɗannan ɗakunan karatu, fasalulluka, da iyakokin su don ƙirƙirar ingantattun abubuwan gani na geodata masu amfani.

• GeoPandas ɗakin karatu ne da aka gina a saman Pandas, wanda aka ƙera a sarari don sarrafa bayanan ƙasa. Yana iya karantawa da rubuta nau'ikan bayanai daban-daban, aiwatar da ayyukan geospatial, da sauƙin haɗawa da sauran ɗakunan karatu na Python kamar Matplotlib don ganin bayanan.
• Folium ɗakin karatu ne wanda ke samar da taswirori masu mu'amala ta amfani da ɗakin karatu na Leaflet JavaScript, wanda ya dace da taswirori na choropleth masu mu'amala da taswirorin zafi. Yana ba da sauƙi mai sauƙi don ƙirƙirar taswira tare da yadudduka daban-daban (alamomi, popups, da sauransu), yana mai da shi kyakkyawan zaɓi ga waɗanda ba ƙwararru ba waɗanda ke son ƙirƙirar taswira masu rikitarwa.
• Makirci Labura ce mai ƙarfi kuma mai juzu'i don ƙirƙirar hotuna masu ma'amala da bugawa, sigogi, da taswira. Plotly Express babban keɓantawa ne don ƙirƙirar waɗannan abubuwan gani cikin sauri, yayin da API ɗin 'graph_objects' da ke da hannu yana ba da damar keɓance kowane dalla-dalla na gani.

Magani ga Matsala: Kallon Geodata Amfani da Python

Bari mu yi la'akari da yanayin gama-gari wanda a cikinsa muke so mu hango yadda ake rarraba yawan jama'a a cikin ƙasashe daban-daban. Za mu yi amfani da bayanan da ke ɗauke da iyakoki a tsarin GeoJSON da yawan yawan jama'a a tsarin CSV. Da farko, muna buƙatar karantawa, sarrafa, da haɗa wannan bayanan. Sa'an nan, za mu ƙirƙiri taswirar choropleth don ganin girman yawa tare da ma'aunin launi masu dacewa.

1. Karanta kuma sarrafa bayanai

Za mu fara da karanta bayanan ta amfani da GeoPandas don bayanan yanki da Pandas don yawan yawan jama'a. Sannan, za mu haɗa waɗannan firam ɗin bayanai guda biyu bisa maɓalli gama gari (misali, lambar ƙasa).

import geopandas as gpd
import pandas as pd

# Read the GeoJSON file
world_map = gpd.read_file("world_map.geojson")

# Read the CSV file with population densities
density_data = pd.read_csv("population_density.csv")

# Merge the dataframes based on the common key (country code)
merged_data = world_map.merge(density_data, on="country_code")

2. Ƙirƙiri Taswirar Choropleth

Amfani da GeoPandas da Matplotlib, za mu iya ƙirƙirar taswirar choropleth don nuna yawan yawan jama'a tare da ma'aunin launi.

import matplotlib.pyplot as plt

# Create a choropleth map using population density data
fig, ax = plt.subplots(1, figsize=(10, 6))
merged_data.plot(column="population_density", cmap="Blues", linewidth=0.8, ax=ax)
plt.show()

Bayanin mataki-mataki na lambar Python

Yanzu da muka sami mafitarmu, bari mu bi ta hanyar code mataki-mataki don fahimtar kowane bangare. Za mu fara da shigo da dakunan karatu masu mahimmanci:

import geopandas as gpd
import pandas as pd
import matplotlib.pyplot as plt

Na gaba, muna karanta fayil ɗin GeoJSON ta amfani da GeoPandas da fayil ɗin CSV ta amfani da Pandas.

world_map = gpd.read_file("world_map.geojson")
density_data = pd.read_csv("population_density.csv")

Bayan haka, muna haɗa firam ɗin bayanai ta maɓalli gama gari, a wannan yanayin, lambar ƙasa.

merged_data = world_map.merge(density_data, on="country_code")

A ƙarshe, mun ƙirƙiri taswirar choropleth ta amfani da GeoPandas da Matplotlib, suna ƙayyadaddun ginshiƙi don hangowa (yawan yawan jama'a) da taswirar launi (Blues).

fig, ax = plt.subplots(1, figsize=(10, 6))
merged_data.plot(column="population_density", cmap="Blues", linewidth=0.8, ax=ax)
plt.show()

Wannan ya ƙare binciken mu na ganin geodata a cikin Python. Mun tattauna dakunan karatu daban-daban, kamar GeoPandas, Folium, Da kuma Makirci, da ayyukansu wajen ƙirƙirar abubuwan gani na geodata masu ƙarfi da ma'amala. Tare da wannan ilimin, ya kamata ku zama mafi kyawun kayan aiki don magance hadaddun ayyukan gani na geodata da haɓaka ingantattun mafita.