Calculate complexity in each of subway stations at various times

Introduction

There are three steps to get a population in each of subway stations.

1. Get datasets
2. Preprocess datasets
3. Visualize distribution of population

Datasets

Input Datasets

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① The number of passenger information in Seoul subway station based on an one hour

• Structure

upd_date	line_num	station_name	board_pass_f04_t05	getoff_pass_f04_t05	...
202101	1호선	동대문	542	15	...

upd_date: when this information updated.

line_num: a Line number of subway station

station_name : a name of subway station

boardpass_f 'timestart' _t 'time_end' : the number of population who get into subway from 'time_start to 'time_end'

getoffpass_f 'timestart' _t 'time_end' : the number of population who get off from 'time_start to 'time_end'

address: http://data.seoul.go.kr/dataList/OA-12252/S/1/datasetView.do

② The location of subway station in Seoul

• Structure

station_name	Latitude	longitude
동대문	37.571284	127.008981

station_name : a name of subway station

Latitude : the Latitude in each of stations

longitude : the longitude in each of stations

Preprocessed Dataset

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① the dataset that have two input datasets' informations above the passage.

• Structure

station_name	Latitude	longitude	from06_to10	from10_to14	...	from02_to06
동대문	37.571284	127.008981	25024.50	31739.25	...	3742.75

station_name : a name of subway station

Latitude: the Latitude in each of stations

longitude : the longitude in each of stations

from06_to10 : the population in each of stations from 6AM to 10AM

from10_to14 : the population in each of stations from 10AM to 02PM

. . .

from02_to06 : the population in each of stations from 02AM to 06AM

• A picture of Preprocessed Dataset

Function

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• quantize_station_passengers

  return the list of tuples which are formed like (city name: string, population: int) by using pandas