An app that analyzes data from cities such as populations, cost of living, rental rates, crime rates, park (walk score), and many other social and economic factors that are important in deciding where someone would like to live. This app will present such important data in an intuitive and easy to understand interface. Use data to find a place right for you to live.
- Clone repository
- In terminal run the following code to deploy:
pipenv install --devpipenv shelluvicorn app.main:app --reload
- Go to
localhost:8000in your browser, and follow the instructions you see there!- With the visualization endpoints, when checking locally, another tab will pop up displaying an interactive plotly graph.
http://ec2-35-153-93-234.compute-1.amazonaws.com:8000/
- /api/info This is the link to the AWS RDS Postgres database.
- /api/get_data Here users can query cities and get an overview of the city with recommendations for similar cities
{
"city": {
"city": "San Francisco",
"state": "CA"
},
"latitude": 37.775,
"longitude": -122.4183,
"rental_price": 3700,
"crime": "High",
"air_quality_index": "Good",
"population": 886007,
"diversity_index": 69,
"walkability": 87,
"livability": 50,
"recommendations": [
{
"city": "Oakland",
"state": "CA"
},
{
"city": "Portland",
"state": "OR"
},
{
"city": "San Diego",
"state": "CA"
},
{
"city": "San Jose",
"state": "CA"
},
{
"city": "Seattle",
"state": "WA"
}
]
}
- /api/coordinates Here users can get coordinates for the city, this is used by frontend so users can visualize the city location
{
"latitude": 37.775,
"longitude": -122.4183
}
- /api/crime Here users get a low, medium, high range for crime in specific cities based on the FBI crime database
{
"crime": "High"
}
- /api/rental_price Here users can see the estimate for rental prices in the city they are interested in
{
"rental_price": 1500
}
- /api/pollution This endpoint allows users to gauge the pollution based on the aqi index
{
"air_quality_index": "Good"
}
- /api/walkability This gives users an idea of how walkable their city is.
{
"walkability": 87
}
- /api/livability This endpoint gives users an estimate of livability based on user preference
{
"city": {
"city": "San Francisco",
"state": "CA"
},
"weights": {
"walkability": 1,
"low_rent": 1,
"low_pollution": 1,
"diversity": 1,
"low_crime": 1
}
}
{
"livability": 56
}
- /api/population This endpoint gives users the population information for their city
{
"population": 886007
}
- /api/nearest This endpoint allows users to find similar cities to the one they are interested in moving to
{
"recommendations": [
{
"city": "Oakland",
"state": "CA"
},
{
"city": "Portland",
"state": "OR"
},
{
"city": "San Diego",
"state": "CA"
},
{
"city": "San Jose",
"state": "CA"
},
{
"city": "Seattle",
"state": "WA"
}
]
}
Below are the visualization endpoints that allows users to visualize and get a better sense of the cities datapoints
-
/api/demographics_graph
-
/api/employment_graph
-
/api/crime_graph
-
/api/aqi_graph
Below are links to resources used to create this project:
-
Calculating Livability Index: https://github.com/Lambda-School-Labs/labspt15-cityspire-g-ds/tree/main/notebooks/model/livability This is how we created the livability index for users (livability.pkl)
-
Nearest Neigbhors Model: https://github.com/Lambda-School-Labs/labspt15-cityspire-g-ds/tree/main/notebooks/model/nearest_neighbor This is how we created the recommendations for cities using the nearest neighbors model
The diversity indext was calculated using Simpson's Diversity Index D = 1 - ((Σ n(n-1)/ (N(N-1))
- n = numbers of individuals of each ethnicity
- N = total number of individuals of all ethnicities
- The value of D ranges between 0 and 1
To determine overall air quality for each city, we used the median value and then created an algorithm that separated it based on https://www.airnow.gov/aqi/aqi-basics/ index.
Crime Rate per 1,000 inhabitants: This represents the number of Index offenses per 1,000 inhabitants.For example: What is the crime rate for a municipality with 513 Index offenses (murder, rape, robbery,aggravated assault, burglary, larceny-theft and motor vehicle theft), with a population of 8,280?
513 (Index offenses) ÷ 8,280 (population) = .061957 x 1,000 = 62.0 (crime per 1,000 inhabitants)
https://www.njsp.org/info/ucr2000/pdf/calc_ucr2000.pdf
(https://github.com/Lambda-School-Labs/labspt15-cityspire-g-ds/tree/main/notebooks/datasets/datasets_to_merge)
The first link shows all combined csv that is stored in Postgres.
(https://github.com/Lambda-School-Labs/labspt15-cityspire-g-dsli/tree/main/notebooks/datasets/data)
This link shows all the different datasets we used to compile our data about the different cities.
https://github.com/Lambda-School-Labs/labspt15-cityspire-g-ds/tree/main/app
This is where the code for creating the different endpoints can be located.
https://github.com/Lambda-School-Labs/labspt15-cityspire-g-ds/blob/main/Pipfile
Tech Stacks
- Python
- SQL
- Scikit
- AWS RDS PostgreSQL: Relational database service.
- AWS Elastic Beanstalk: Platform as a service, hosts your API.
- Docker: Containers, for reproducible environments
- FastAPI: Web framework. Like Flask, but faster, with automatic interactive docs.
- Plotly: Visualization library, for Python & JavaScript.
- Pytest: Testing framework, runs your unit tests.
Data Sources:
- US Census - https://www.census.gov/
- FBI Crime Data - https://ucr.fbi.gov/crime-in-the-u.s/2019/crime-in-the-u.s.-2019/tables/table-8/table-8-state-cuts/arizona.xls/@@template-layout-view?override-view=data-declaration
- Pollution Data - https://aqs.epa.gov/aqsweb/airdata
- Rental Data - https://www.huduser.gov/
- Walkscore - https://www.walkscore.com/
| Jisha Obukwelu | Erik Seguinte | dataabyss | Keino Baird |
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