Addresses-distance 🗺️📍📐

Get information about the distance of directions to a known address.

How to use the Application?

1. Verify you have installed Node.js before run the app, works well on v12.13.0, check version with node -v. If you don't have it installed, you can download it from Node.js.
2. Run npm install to install the dependencies -only the first time you build the app-.
3. Place your own csv file called AddressInput.csv on root with the addresses you want to calculate the distance.
4. Run npm start to run the process.
5. At the end, process will create AddressesOutput.csv file on root with data ordered by distance given reference indicated in the file. In case of errors processing some addresses, AddressesOutputErrors.csv will be created with the addresses that could not be processed.

Csv Input file

AddressesInput.csv file should be present on root and should have the following format:

Name	Address	City	Country
Origin	Dr. Luis Bonavita 1294	Montevideo	Uruguay
Montevideo Shopping	Av. Luis Alberto de Herrera 1290	Montevideo	Uruguay
Punta Carretas Shopping	Jose Ellauri 350	Montevideo	Uruguay
Nuevo Centro Shopping	Av. Luis Alberto de Herrera 3365	Montevideo	Uruguay
Portones Shopping	Av. Italia 5775	Montevideo	Uruguay
G Punta del Este	Avenida Roosevelt y 20100	Maldonado	Uruguay
G Medellin	Torre Ilustre	Medellin	Colombia

• Values are separated by , and the file should have a header with the columns names detailed.
• Name and Address are mandatory fields, City and Country are optional, in case of null || undefined, Montevideo, Uruguay will be taken.
• Row with Name = Origin will be considered the reference to calculate distance from others points.

Csv Output files

Addresses successfully compared

AddressesOutput.csv file will be created on root with the following format:

Position	Name	Distance in Kilometers	Address	Suburb	PostCode	City	Country	AddressLat	AddressLon
1	Montevideo Shopping	0.180	Av. Luis Alberto de Herrera 1290	Buceo	11300	Montevideo	Uruguay	-34.9030628	-56.13636551266029
2	Punta Carretas Shopping	3.263	Jose Ellauri 350	Punta Carretas	11303	Montevideo	Uruguay	-34.9237331	-56.159439
3	Nuevo Centro Shopping	4.982	Av. Luis Alberto de Herrera 3365	Jacinto Vera	12000	Montevideo	Uruguay	-34.8684338	-56.1695721
4	Portones Shopping	5.356	Av. Italia 5775	Punta Gorda	11500	Montevideo	Uruguay	-34.8814063	-56.0817779

• This file contains the addresses well processed ordered ascending by distance in kilometers against the reference point indicated as Origin in AddressesInput.csv.

Addresses incorrectly processed

AddressesOutputErrors.csv file will be created on root if errors occurs with the following format:

Position	Name	Address	City	Country
1	G Punta del Este	Avenida Roosevelt y 20100	Maldonado	Uruguay
2	G Medellin	undefined undefined	undefined	undefined

• This file contains the addresses that could not be processed due to an error in the address or the geolocation service could not find the address.
  • Some known issues are:
    • Special characters in the address
    • Address not found
    • Address not well formatted
    • Address not well geolocated

Behind the hood

• Geolocate is a process to find the geographic location of an object in the world, the API used to Geolocate addresses is Nominatim from the OpenStreetMap.
• The way distance is calculated currently is using the Haversine formula.
  • By the way it works, formula will underestimate distances in some situations.
    • E.g.: From London Eye to the Korean War Memorial, using haversine distance you would think it’s only 300 metres away. However, once you account for the Thames in the middle and decide you will not try to swim through it, you’ll find that the distance is actually three times what you thought, 900 metres.
• WIP to replace the Haversine formula to use Open Source Routing Machine to calculate the distance avoiding underestimation. Other possibility is provid our own GeoServer instance for geo-spacial operations 💚.

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