Jan Tschada gisfromscratch

## berlin_starting_locations.geojson

      
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                gisfromscratch
                / berlin_starting_locations.geojson
            
            
              Created
              July 14, 2024 15:38
            
              
                Berlin S- and U-Bahn stations near the Olympiastadium
              
          
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## walking_scenarios.py
from arcgis.gis import GIS
from arcgis.features import FeatureLayer, FeatureSet
from arcgis.mapping import WebMap
from geopedestrian.categories import CategoryRegistry
from geopedestrian.services import drive_from, solve_walking, walk_to
from georapid.client import GeoRapidClient
from georapid.factory import EnvironmentClientFactory
import os
import pandas as pd

## simulated_car_positions.md

      
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                gisfromscratch
                / simulated_car_positions.md
            
            
              Last active
              January 14, 2024 14:45
            
              
                The simulated agent positions representing high commute car traffic nearby a highway crossroad intersection
              
          
trip
person
trip_time
SHAPE


3485
2168
2023-08-24T08:00:00
...


...
...
...
...


75
46
2023-08-24T08:00:00
...


...
...
...
...


75
46
2023-08-24T08:00:12
...


...
...
...
...


1562
966
2023-08-24T08:00:29
...


## top_traffic_grid_cells.md

      
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                gisfromscratch
                / top_traffic_grid_cells.md
            
            
              Created
              January 11, 2024 01:07
            
              
                The top five accumulated car traffic grid cells from the city of Bonn
              
          
start_time
end_time
agent_count
SHAPE


2023-08-24T08:00:00
2023-08-24T08:59:59
439
...


...
...

...


...
...

...


...
...

...


2023-08-24T09:00:00
2023-08-24T09:59:59
408
...


## pythagorean.py
import math

# Function to calculate the hypotenuse
def calculate_hypotenuse(a, b):
    return math.sqrt(a**2 + b**2)

# Function to calculate one leg (a)
def calculate_leg_a(c, b):
    return math.sqrt(c**2 - b**2)

## geoint-mapping-russian-invasion.py
# Mapping the aggregated armed conflict events
focus_map = create_osm_webmap()
aggregated_events_renderer = create_aggregated_renderer(aggregated_events_featureset.sdf)
focus_map.add_layer(aggregated_events_featureset, {'renderer': aggregated_events_renderer, 'opacity': 0.7})
focus_map

# Mapping the armed conflict events
focus_map = create_osm_webmap()
events_renderer = create_events_renderer()
focus_map.add_layer(events_featureset, {'renderer': events_renderer, 'opacity': 0.7})

## geoint-russian-invasion.py
host = 'geoconflicts.p.rapidapi.com'
conflicts_client = EnvironmentClientFactory.create_client_with_host(host)

features_dict = query(conflicts_client, date=datetime(2022, 2, 24), format=OutFormat.ESRI)
events_featureset = FeatureSet.from_dict(features_dict)

aggregated_features_dict = aggregate(conflicts_client, date=datetime(2022, 2, 24), format=OutFormat.ESRI)
aggregated_events_featureset = FeatureSet.from_dict(aggregated_features_dict)

## geoint-utils.py
def create_osm_webmap():
    """Creates a simple web map using the OSM basemap."""
    webmap = WebMap()
    webmap.basemap = 'osm'
    return webmap

def create_events_renderer():
    """Creates a simple renderer representing events as areas of interests."""
    renderer = generate_simple(geometry_type='Polygon', colors=[171, 31, 42, 191])
    renderer_outline = {

## geoint-imports.py
from arcgis.gis import GIS
from arcgis.features import FeatureLayer, FeatureSet
from arcgis.mapping import WebMap
from arcgis.mapping.renderer import generate_simple, generate_classbreaks
from datetime import datetime
from georapid.client import GeoRapidClient
from georapid.factory import EnvironmentClientFactory
from georapid.conflicts import aggregate, query
from georapid.formats import OutFormat

## locations-25-26-02-2022.md

      
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                gisfromscratch
                / locations-25-26-02-2022.md
            
            
              Created
              March 5, 2022 03:32
            
              
                Protests locations 25-26 February 2022
              
          
name
count
date


Moscow, Moskva, Russia
797
2022-02-25


Budapest, Budapest, Hungary
258
2022-02-25


Kiev, Ukraine (General), Ukraine
256
2022-02-25


Saint Petersburg, Sankt-Peterburg, Russia
197
2022-02-25


New Delhi, Delhi, India
178
2022-02-25


Berlin, Berlin, Germany
118
2022-02-25


Dublin, Dublin, Ireland
83
2022-02-25


London, London, City Of, United Kingdom
73
2022-02-25
	from arcgis.gis import GIS
	from arcgis.features import FeatureLayer, FeatureSet
	from arcgis.mapping import WebMap
	from geopedestrian.categories import CategoryRegistry
	from geopedestrian.services import drive_from, solve_walking, walk_to
	from georapid.client import GeoRapidClient
	from georapid.factory import EnvironmentClientFactory
	import os
	import pandas as pd
trip	person	trip_time	SHAPE
3485	2168	2023-08-24T08:00:00	...
...	...	...	...
75	46	2023-08-24T08:00:00	...
...	...	...	...
75	46	2023-08-24T08:00:12	...
...	...	...	...
1562	966	2023-08-24T08:00:29	...
start_time	end_time	agent_count	SHAPE
2023-08-24T08:00:00	2023-08-24T08:59:59	439	...
...	...		...
...	...		...
...	...		...
2023-08-24T09:00:00	2023-08-24T09:59:59	408	...
	import math

	# Function to calculate the hypotenuse
	def calculate_hypotenuse(a, b):
	return math.sqrt(a2 + b2)

	# Function to calculate one leg (a)
	def calculate_leg_a(c, b):
	return math.sqrt(c2 - b2)
	# Mapping the aggregated armed conflict events
	focus_map = create_osm_webmap()
	aggregated_events_renderer = create_aggregated_renderer(aggregated_events_featureset.sdf)
	focus_map.add_layer(aggregated_events_featureset, {'renderer': aggregated_events_renderer, 'opacity': 0.7})
	focus_map

	# Mapping the armed conflict events
	focus_map = create_osm_webmap()
	events_renderer = create_events_renderer()
	focus_map.add_layer(events_featureset, {'renderer': events_renderer, 'opacity': 0.7})
	host = 'geoconflicts.p.rapidapi.com'
	conflicts_client = EnvironmentClientFactory.create_client_with_host(host)

	features_dict = query(conflicts_client, date=datetime(2022, 2, 24), format=OutFormat.ESRI)
	events_featureset = FeatureSet.from_dict(features_dict)

	aggregated_features_dict = aggregate(conflicts_client, date=datetime(2022, 2, 24), format=OutFormat.ESRI)
	aggregated_events_featureset = FeatureSet.from_dict(aggregated_features_dict)
	def create_osm_webmap():
	"""Creates a simple web map using the OSM basemap."""
	webmap = WebMap()
	webmap.basemap = 'osm'
	return webmap

	def create_events_renderer():
	"""Creates a simple renderer representing events as areas of interests."""
	renderer = generate_simple(geometry_type='Polygon', colors=[171, 31, 42, 191])
	renderer_outline = {
name	count	date
Moscow, Moskva, Russia	797	2022-02-25
Budapest, Budapest, Hungary	258	2022-02-25
Kiev, Ukraine (General), Ukraine	256	2022-02-25
Saint Petersburg, Sankt-Peterburg, Russia	197	2022-02-25
New Delhi, Delhi, India	178	2022-02-25
Berlin, Berlin, Germany	118	2022-02-25
Dublin, Dublin, Ireland	83	2022-02-25
London, London, City Of, United Kingdom	73	2022-02-25