The Battle of Neighborhoods  Business Proposal .ipynb

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    "# The Battle of Neighborhoods | Business Proposal | "
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   "source": [
    "## Introduction:"
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   "source": [
    "This project aims at creating a tool that can be used to explore facilities in and around neighbourhoods in a way that allows residents to make informed decisions and select the most optimal location that best caters to their needs. \n",
    "\n",
    "An example of such need could be smaller cities and towns in various provinces of Canada. Canada has had a long and successful immigration policy that allows regulating the country’s population. From its onset in 1870, the influx of immigrants has been tailored to grow the population, settle the land, and provide labour and financial capital for the economy. However, in a democratic country, such as Canada, both the newcomers and the existing residents have a right to move around and select a location with the housing prices, good schools, medical facilities and job opportunities that suit their needs and financial means. \n",
    "\n",
    "This project would be highly beneficial for those who seek a systematic, algorithm-based approach for comparison and selection of the desired parameters, e.g. access to public or catholic schools, supermarkets, shopping malls, movie theaters, hospitals, or religious communities. This selection can be made using analytical tools rather than basic internet searches, reliance on real estate agencies or word of mouth that could be biased or misleading, whether intentionally or not. Studies such as [1] show that people need to think critically about claims and compare their options using a systematic concept because simply trusting the sources of information may not be a sufficient basis for making the best choice:\n",
    "-\tCompeting interests can result in misleading claims.\n",
    "-\tPersonal experiences or anecdotes alone are an unreliable basis for most claims.\n",
    "-\tOpinions of acquittances, real-estate agents or newspaper/social media publications are not solely a reliable basis for making informed-decisions.\n",
    "-\tEndorsements by community leaders or other respectable individuals do not guarantee that comparisons have been fair.\n",
    "\n",
    "Specifically, this project looks at a community of Russian-speaking expatriates who are looking to re-settle in the Greater Toronto Area (GTA) or in other smaller neighbouring communities in the province of Ontario. This project will propose a tool that can be used to perform searches and comparisons between various neighbourhoods in order to determine a community with specific desired parameters, e.g., access to grocery stores and marketplaces where Russian-produced goods and food supplies are available. \n",
    "\n",
    "Additional parameters could include ease of access to children’s daycares and after-school activities where Russian language is taught. Another important factor could be availability of various medical facilities (e.g., Russian-speaking family doctors, dentists, phycologists and other medical specialists) for Russian-speaking patients and access to nursing homes where services in Russian language are available for seniors. Restaurants offering traditional Russian cuisine is another important factor that may sway a customer’s decision to relocate to a particular community.\n",
    "\n",
    "Other selection criteria could be median housing prices, school ratings, community-specific crime rates, transportation, and availability of recreational facilities.\n"
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   "source": [
    "## Problem to Solve:"
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    "The main objective of this project is to propose an approach for selection of an optimal community in a new city for someone looking to relocate. The individual using this proposed tool would be able to compile, sort and filter parameters of interest, for example:\n",
    "\n",
    "1.\tA list of houses/residencies that can be sorted in terms of housing prices in an ascending or descending order, or\n",
    "2.\tA list of restaurants that cater particular type of cuisine that can be sorted in terms of location, prices, rating and reviews.\n"
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   "source": [
    "## The Location:"
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    "The problem for the Canadian province of Ontario is that most immigrants to the province make their home in the GTA, specifically 77 per cent, according to an August report from the Conference Board of Canada [2]. However, as their populations age and young adults move away, small cities and towns across Canada are increasingly looking to immigration as a way to rejuvenate their workforce and expand their tax base. But many struggle to find reliable data and make informed decision to move to smaller rural areas due to lack of data and unavailability of unbiased selection tools. The city of Waterloo, Ontario  will be used in this project to demonstrate the proposed approach as opposed to Toronto."
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   "source": [
    "## Foursquare API:"
   ]
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   "source": [
    "This project would use Four-square API as its prime data gathering source as it has a database of millions of places, especially their places API which provides the ability to perform location search, location sharing and details about a business."
   ]
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   "source": [
    "## Work Flow:"
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   "source": [
    "Using credentials of Foursquare API features of near-by places of the neighborhoods would be mined. Due to http request limitations the number of places per neighborhood parameter would reasonably be set to 100 and the radius parameter would be set to 500."
   ]
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   "source": [
    "## Clustering Approach:"
   ]
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   "source": [
    "To compare the similarities of two cities, it was decided to explore neighborhoods, segment them, and group them into clusters to find similar neighborhoods in a big city like Waterloo and Toronto. To be able to do that, it is necessary to cluster data which is a form of unsupervised machine learning: k-means clustering algorithm."
   ]
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   "source": [
    "## Libraries Used to Develop the Project:"
   ]
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   "source": [
    "Pandas: For creating and manipulating dataframes.\n",
    "Folium: Python visualization library would be used to visualize the neighborhoods cluster distribution of using interactive leaflet map.\n",
    "Scikit Learn: For importing k-means clustering.\n",
    "JSON: Library to handle JSON files.\n",
    "XML: To separate data from presentation and XML stores data in plain text format.\n",
    "Geocoder: To retrieve Location Data.\n",
    "Beautiful Soup and Requests: To scrap and library to handle http requests.\n",
    "Matplotlib: Python Plotting Module.\n"
   ]
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   "source": [
    "## References"
   ]
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   "source": [
    "[1] Jeffrey K. Aronson, “Key concepts for making informed choices”, A Nature Research Journal, 12 August 2019 \n",
    "\n",
    "[2] https://www.conferenceboard.ca/e-library/abstract.aspx?did=10342&AspxAutoDetectCookieSupport=1\n"
   ]
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