NodeJS library for geographic localization using k-gon clouds in fixed distance.
Many applications do things like:
- Give me a list of all the restaurants within 5 miles of where I am.
- Give me a list of all the markers people have left within a 10 mile radius of where I am.
- Give me a list of all the photos people have taken within 5 blocks of where I am.
These questions are structured as give me a list of all the Xs are near Y.
Or, given a fixed distance from some query coordinate, give me a list of all location coordinates.
The brainiac structure solves this problem with an augmented BST based on k, referring to the k-Gon clouds placed "about" every location coordinate, and d, the fixed distance.
The brainiac structure uses the haversine formula to compute distance between coordinates.
Read more about the project here.
##usage
var b = require('brainiac');
var brain = b.brainiac(k,d);
// Add coordinates
brain.add(40.7974,-74.481536);
brain.add(34.020029,-118.286931);
brain.add(29.426468,-98.491233);
brain.add(37.62261,-122.37804);
brain.add(61.59938,-149.126804);
brain.add( 39.653671,-104.959502);
brain.add(47.850015,-122.279457);
brain.add(52.114942,-106.632519);
brain.add(47.622767,-122.33668);
brain.add(41.643112,-88.001369);
// Query against the structure
var query = brain.query(41.643155,-88.001322)
// Return queries are of the following format
query === [
{
latitude: 41.643112,
longitude: -88.001369,
borders: [...]
},
...
]##efficiency
Compare the brainiac structure to some simple method of iterating through all location coordinates and computing distance to some query coordinate.
The brainiac structure can be written as:
Search an augmented binary search tree for a longitude interval.
Search some augmented binary search tree for a latitude interval over some longitude interval.
For all members of a node's data set within that tree:
Compare haversine distance to some query coordinate.
If less than d, add to return list.
Return return list.
Or, in a time complexity analysis as follows:
O(logn) + O(logn) + O(m)
Where m is the average density of these created "interval squares".