stiles

import geopandas as gpd
import matplotlib.pyplot as plt

# Load the shapefile or GeoJSON file for Los Angeles
la_boundary = gpd.read_file('https://maps.lacity.org/lahub/rest/services/Boundaries/MapServer/7/query?outFields=*&where=1%3D1&f=geojson').to_crs('EPSG:4326')

# Reproject to a suitable projected CRS (EPSG:3310 for California Albers)
la_boundary_projected = la_boundary.to_crs(epsg=3310)

# Calculate the centroid in the projected CRS

stiles

stiles

id,conversation_id,text,source,in_reply_to_user_id,geo,retweet_count,reply_count,like_count,quote_count,date
1543379427505078273,1543379427505078273,"RT @spicetradeai: $1 in january is now $0.9618 due to inflation

CPI rose to 292.296 in May from 289.109 in April. To counteract inflation,…",Twitter for iPhone,,,10,0,0,0,2022-07-02
1543379333573554176,1543378085592137730,@davidjvickery Hehehehe,Twitter for iPhone,4790227152,,0,4,2,0,2022-07-02
1543378758135992320,1543378085592137730,@IcedKnife Have to keep the lights on,Twitter for iPhone,618539620,,0,0,17,0,2022-07-02
1543371121906462720,1543369905403088896,@WaspR6 Peoples champ,Twitter for iPhone,941797412380774400,,6,1,770,0,2022-07-02
1543369905403088896,1543369905403088896,Sit on my face and use my ears as handle bars.,Twitter for iPhone,,,3867,789,24854,2657,2022-07-02
1543318984933576705,1543318984933576705,"New lisitngs this week came in hot🔥 

stiles

stiles

stiles

stiles

stiles

stiles

stiles

{
"type": "FeatureCollection",
"crs": { "type": "name", "properties": { "name": "urn:ogc:def:crs:OGC:1.3:CRS84" } },
"features": [
{ "type": "Feature", "properties": { "zipcode": "90001", "name": "Los Angeles", "domcounty": "06037", "totpop_cy": 59907, "tothh_cy": 13454, "medage_cy": 27.1, "avghinc_cy": 46931, "avgval_cy": 377591, "tie": 0, "chan_jeong": 0, "herb_wesson": 2500, "jan_perry": 0, "holly_mitchell": 0, "jorge_nuno": 0, "albert_robles": 0, "total": 2500, "winner_dist_two": "Herb Wesson", "winner_total": 2500 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -118.247540000384006, 33.989080000253303 ], [ -118.247760000491994, 33.9893400001475 ], [ -118.247760000491994, 33.9886000017962 ], [ -118.256460000762004, 33.9886000017962 ], [ -118.256460000762004, 33.9893400001475 ], [ -118.259959999703995, 33.989290001891199 ], [ -118.259870000191, 33.987189999401103 ], [ -118.259809999916996, 33.985670000659603 ], [ -118.265190000272, 33.985630000776403 ], [ -118.265190000272, 33.981990001137198 ], [