Kathmandu Metropolitan city and Lalitpur Metropolitan City are two of the biggest cities of Nepal. They act like a combined mega city for the residents of these two cities.
Kathmandu Metropolitan city and Lalitpur Metropolitan City are two of the biggest cities of Nepal. Although being two different cities, they act like a combined mega city for the residents of these two cities. Everyday more than thousands of people commutes between these two cities as a part of their normal routine.
There are 32 municipal wards and 29 municipal wards in Kathmandu Metropolitan city and Lalitpur Metropolitan city respectively. For this project, we want to classify different wards of these two cities on the basis of the venues present in the wards. Our goal is to study both cities as a single combined city and seek out the wards of these two cities which mirrors each other or acts as a single unit.
As densely populated area are generally more expensive and noisy, we are also looking at the population density data of each wards. This research will be really insightful for an individual who wants to move or have to move inside Kathmandu or Lalitpur city. This analysis will help them to narrow out the wards on basis of their preference on venues and population density.
We need to collect data regarding wards locations, population density and area for both Kathmandu and Lalitpur city.
As we are collecting data for two cities, we will collect data for each city individually and later merge them as a single dataset. Data for individual city is scrapped and collected from data provided in their respective metropolitan city websites. Almost all data about wards areas, population and location are provided in Nepali (Devanagari) language, so we have to translate data into English language using Google translate and then scrap the data.
The location of the head offices of each wards is provided by both cities official site. We will use that location data to locate the latitude and longitude of wards using geocoder library.
Venues details for each ward can be obtained using latitude, longitude data and four square API.
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