Purpose:

Build multiscale graph where nodes are regions (City, State, Country, Continent) for convid19 simulation. Graph structure (within-scale and across-scale) and node initial static properties (centroids, population, #airports, #train station, #ferry ports) can be built.

Regions

Name	Description
Region type	{Airport, Train Station, Ferry Port, City, State, Country, Continent}
Graph nodes	{City, State, Country, Continent}

Note:

• City contains Airport, Train Station and Ferry Port.
• State contains City.
• Country contains State.
• Continent contains Country.

Region Property

Centriods: Centriods are in form of (Latitude, Longitude):

• Every Airport has Centriods
• Every Train Station has Centriods
• Every Ferry Port has Centriods
• Not Every City has Centriods (1460 cities have no Centriods)
• Not Every State has Centriods (1820 states have no Centriods)
• Not Every Country has Centriods (58 countries have no Centriods)
• NONE Continent has Centriods

Population:

• Only City has Population so far
• Not Every City has Population (1460 cities have no Population)

Underlying Across-scale Region Connection

From high-resolution to coarse-resolution:

• Every Airport has parent City
• Every Train Station has parent City
• Every Ferry Port has parent City
• Every City has parent State
• Every State has parent Country
• Not Every Country has parent Continent (15 countries have no Continent)

From coarse-resolutio to high-resolution:

• Not Every Continent has children Country ("Antarctica" has no Country)
• Not Every Country has children State (24 countries have no State)
• Not Every State has children City (612 states have no City)
• Every City has at least a child Airport
• Not Every City has a child Train (2879 cities have no Train)
• Not Every City has a child Ferry (3086 cities have no Ferry)

Underlying Within-scale Region Connection

Train-scale:

• Disconnected (no train route information)

Ferry-scale:

• Disconnected (no ferry route information)

Airport-scale:

• Every arport is connected to another airport with direction (number of flights: 66133)

City-scale:

• Every city is connected to another city with direction (Specifically, city i connects to city j if there is a flight from city i to city j)

State-scale:

• Is every state is connected to another state with direction? (Specifically, state i connects to state j if there is a flight from state i to state j)

Country-scale:

• Is every country is connected to another country with direction? (Specifically, country i connects to country j if there is a flight from country i to country j)

Continent-scale:

• NOT Every continent is connected to another continent with direction (Specifically, continent i connects to continent j if there is a flight from continent i to continent j). "Antarctica" is isolated at the Continent-scale. Is there any other continent isolated?

How Regions obtained

Final number of flights: 66133

Region type	Description	Number
Airport	{all airports simultaneously have flights, city, state information}	3227
Train	{all train stations that all cities that we consider have}	478
Ferry	{all ferry ports that all cities that we consider have}	34
City	{all cities have flights and have state information}	3114
State	{states of all cities} + {all states in JHU COVID-19 database}	1955
Countries	{countries of all states} + {all countries in JHU COVID-19 database}	231
Continent	{all countries' continent}. Allowed values are: "Africa" (AF), "Antarctica" (AN), "Asia" (AS), "Europe" (EU), "North America" (NA), "Oceania" (OC), "South America" (SA).	7

Basically:

1. Find all cities have flights and have state information, according to flight_routes_processed_v2.csv, regions.csv, airports.csv and airports.json (because for every city in our graph, we have to know its state and its airports). Meanwhile find all airports.
2. Generate flight.txt based on filtered current all cities and flight_routes_processed_v2.csv.
3. Find current all cities' train stations, ferry ports respectively according to airports-extended.dat, airports-extended.dat. Meanwhile find all train stations, ferry ports.
4. Find all states = {states of current all cities} + {all states in JHU COVID-19 database} (source: regions.csv, airports.json and UID_ISO_FIPS_LookUp_Table.csv).
5. Find all countries = {countries of current all states} + {all countries in JHU COVID-19 database} (source: regions.csv, airports.json and UID_ISO_FIPS_LookUp_Table.csv).
6. Find all continents = {actual all continents} (source: regions.csv and airports.json).

Data source

File name	Data source	Description
UID_ISO_FIPS_LookUp_Table.csv	JHU COVID-19 database	Mapping from Country name to iso2, iso3, Province. When province information is available, the latitude (lat) and longitude (lon) is the province's lat and lon; otherwise, is country's.
airports-extended.dat	OpenFlights	Information for terminals (type value "airport" for air terminals, "station" for train stations, "port" for ferry terminals and "unknown" if not known): name, city, country, lat and lon, type.
airports.csv	OurAirport	Information for airports: name, lat and lon, iso_country, iso_region (e.g. province, governorate), continent.
regions.csv	OurAirport	Information for regions in airports.csv: iso_region, name, iso_country, continent.
countries.csv	OurAirport	Information for countries in airports.csv: iso_country, country_name, continent.
airports.json	tdreyno GitHub	Information for airports: name, lat and lon, city name, state name, country name, etc.
flight_routes_processed_v2.csv	ArcGIS	Information for flight routes: From_Airport, From_Name, From_City, From_Country, From_Latitude, From_Longitude, To_Airport, To_Name, To_City, To_Country, To_Latitude, To_Longitude.
worldcities_processed.csv	ArcGIS	Information for cities: City_Name, Latitude, Longitude, Country, Country_Code_ISO2, Country_Code_ISO3, Population.

Preprocessed dataset

./output/
├── all_train.pickle
├── all_ferry.pickle
├── all_airports.pickle
├── all_cities.pickle
├── all_states.pickle
├── all_countries.pickle
├── all_continents.pickle

├── train2centriods.pickle
├── train2city.pickle

├── ferry2centriods.pickle
├── ferry2city.pickle

├── airport2centriods.pickle
├── airport2city.pickle

├── city2centriods.pickle
├── city2population.pickle
├── city2state.pickle

├── state2centriods.pickle
├── state2country.pickle

├── country2centriods.pickle
├── country2continent.pickle

└── flight.txt

Data name	Data Type	Description
all_train.pickle	set()	A set of all train station names.
all_ferry.pickle	set()	A set of all ferry port names.
all_airports.pickle	set()	A set of all airport names.
all_cities.pickle	set()	A set of all city names.
all_states.pickle	set()	A set of all state names.
all_countries.pickle	set()	A set of all country names.
all_continents.pickle	set()	A set of all continent names.
train2centriods.pickle	dictionary()	Key: train station name; Value: centriods.
ferry2centriods.pickle	dictionary()	Key: ferry port name; Value: centriods.
airport2centriods.pickle	dictionary()	Key: airport name; Value: centriods.
city2centriods.pickle	dictionary()	Key: city name; Value: centriods (can be 'NULL').
state2centriods.pickle	dictionary()	Key: state name; Value: centriods (can be 'NULL').
country2centriods.pickle	dictionary()	Key: country name; Value: centriods (can be 'NULL').
train2city.pickle	dictionary()	Key: train station name; Value: city name.
ferry2city.pickle	dictionary()	Key: ferry port name; Value: city name.
airport2city.pickle	dictionary()	Key: airport name; Value: city name.
city2state.pickle	dictionary()	Key: city name; Value: state name.
state2country.pickle	dictionary()	Key: state name; Value: country name.
country2continent.pickle	dictionary()	Key: country name; Value: continent name (can be 'NULL').
city2population.pickle	dictionary()	Key: city name; Value: population (can be 'NULL').
flight.txt	edgelist	Every line: '{}\t{}\n'.format(source_airport_name, target_airport_name)

Additional notes

To be consistent with JHU COVID-19 database, Country Name for "United States" is "US" in the above preprocessed dataset.