Geodata visualization chishandiso chine simba chinotitendera kuti tinzwisise maitiro akaomarara uye hukama pakati penzvimbo uye imwe data. Inobatsira mukuita sarudzo dzine ruzivo uye kupa data nenzira inowanika uye inobata. Muchinyorwa chino, tichaongorora kuti geodata visualization ingawanikwe sei uchishandisa Python, imwe yemitauro inosiyana-siyana yekuronga iripo nhasi. Tichaongorora maraibhurari akasiyana, mabasa, uye matekiniki anoshandiswa kugadzirisa matambudziko akajairika munzvimbo ino, tive nechokwadi chekuti une hwaro hwakasimba hwekuvakira pairi.
Kuunza Geodata Visualization muPython
Python inopa akati wandei maraibhurari akagadzirirwa chaizvo geodata kuona. Zvimwe zvezvinonyanya kufarirwa zvinosanganisira GeoPandas, Folium, uye rangano. Raibhurari yega yega inoshandisa chinangwa chayo chakasiyana, ichipa mashandiro ayo anogona kushandiswa kugadzira mamepu ane simba uye anodyidzana, machati, uye zvirongwa zvine chekuita negeodata. Semugadziri uye nyanzvi muPython, zvakakosha kuti unzwisise maraibhurari aya, maficha awo, uye zvaasingakwanisi kugadzira zvinobudirira uye mushandisi-ane hushamwari geodata kuona.
- GeoPandas iraibhurari yakavakirwa pamusoro pePandas, yakanyatsogadzirirwa kubata geospatial data. Inogona kuverenga nekunyora akasiyana mafomati edata, kuita geospatial mashandiro, uye nyore kubatanidza nemamwe maPython maraibhurari seMatplotlib yekuona data.
- Folium iraibhurari inogadzira mamepu anodyidzana ichishandisa raibhurari yeLeaflet JavaScript, yakakodzera mamepu echoropleth nemamepu ekupisa. Inopa chimiro chakareruka chekugadzira mamepu ane akasiyana akaturikidzana (mamaki, popups, nezvimwewo), zvichiita kuti ive sarudzo yakanaka kune vasiri nyanzvi vanoda kugadzira mamepu akaoma.
- rangano iraibhurari ine simba uye inoshanda zvakasiyana-siyana yekugadzira inofambidzana uye yakagadzirira kushambadza magirafu, machati, uye mepu. Plotly Express inzvimbo yepamusoro-soro yekugadzira aya ekuona nekukasira, nepo iyo inonyanya kubatanidzwa `graph_objects` API inobvumira kugadzirisa yega yega yekuona.
Mhinduro kune Dambudziko: Kuona Geodata Uchishandisa Python
Ngatitarisei chiitiko chakafanana umo tinoda kuona kugovaniswa kwehuwandu hwevanhu munyika dzakasiyana siyana. Tichashandisa dataset ine geographical miganhu muGeoJSON fomati uye kuwanda kwevanhu mu CSV fomati. Chekutanga, isu tinofanirwa kuverenga, kugadzirisa, uye kusanganisa iyi data. Zvadaro, tichagadzira mepu yechoropleth yekuona madhindindi ane zvikero zvemavara zvakakodzera.
1. Verenga uye Gadzira Data
Tichatanga nekuverenga iyo data tichishandisa GeoPandas yenzvimbo yedata uye Pandas yehuwandu hwevanhu. Zvadaro, tinozobatanidza aya maviri dataframes zvichienderana nekiyi yakajairwa (semuenzaniso, kodhi yenyika).
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. Gadzira Choropleth Mepu
Tichishandisa GeoPandas uye Matplotlib, tinogona kugadzira mepu yechoropleth kuratidza huzhinji hwehuwandu nemakero emavara.
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()
Nhanho-ne-nhanho Tsanangudzo yePython Code
Zvino zvatava nemhinduro yedu, ngatifambei nekodhi nhanho nhanho kuti tinzwisise chikamu chimwe nechimwe. Tinotanga nekuunza kunze ma library anodiwa:
import geopandas as gpd import pandas as pd import matplotlib.pyplot as plt
Tevere, tinoverenga iyo GeoJSON faira tichishandisa GeoPandas uye CSV faira tichishandisa Pandas.
world_map = gpd.read_file("world_map.geojson")
density_data = pd.read_csv("population_density.csv")
Mushure mezvo, isu tinobatanidza dataframes nekiyi yakajairwa, mune iyi kesi, iyo kodhi yenyika.
merged_data = world_map.merge(density_data, on="country_code")
Chekupedzisira, isu tinogadzira mepu yechoropleth tichishandisa GeoPandas uye Matplotlib, tichitsanangura koramu yekuona (huwandu hwevanhu) uye mepu yemavara (Blues).
fig, ax = plt.subplots(1, figsize=(10, 6)) merged_data.plot(column="population_density", cmap="Blues", linewidth=0.8, ax=ax) plt.show()
Izvi zvinopedzisa kuongorora kwedu kwe geodata kuona muPython. Takakurukura maraibhurari akasiyana, akadai GeoPandas, Folium, uye rangano, uye mashandiro avo mukugadzira zvine simba uye zvinopindirana geodata zviono. Neruzivo urwu, iwe unofanirwa kuve wakashongedzerwa zvirinani kubata yakaoma geodata yekuona mabasa uye kugadzira mamwe anoshanda mhinduro.
