Welcome to the API of placeSpace here are some resources that will help you understand the basics of all our APIs. There are three different endpoints that connect to our servers and load the data necessary for the analysis, these are expecified bellow:
Returns all amenities within London
the API returns data containing the latitude, longitude, amenity and type. The parameters needed are as follows:
- type: The general group of amenities (e.g. Shopping, Services, etc.). If type is specified to "all" the API returns all amenities. If type is specified to "summary" the API returns the number of occurrences of all amenities.
- amenity: The name of the amenity (e.g. Restaurant, Parking, etc.). If amenity is specified to "all" the API returns all amenities within the type specified. This parameter is ignored if type is set to summary
- lat: Latitude of center point of area of interest. This parameter is ignored it type is set to summary
- lon: Longitude of center point of area of interest. This parameter is ignored if type is set to summary
- r: radius of area of interest. This parameter is ignored if type is set to summary
Returns network built from a Spearman Rank Correlation matrix of all amenities
The Api returns data containing all amenity pairs with Spearman's rank correlation coefficient (rho). The data relates to an adjacency list of an undirected graph where amenity pairs are origin and target nodes. Because of this the data contains a binary variable that specifies if edge is part of the maximum spanning tree. The parameters needed are as follows:
- rTreshold: a treshold value for Spearman's rank correlation coefficient. The API returns all amenity pairs that have a correlation greater than the value specified.
- MST: a bolean (true, false) value that specifies if data requested is only for the maxiumum spanning tree
Returns Polygons of Clustering Results
The API returns data containing polygons generated from density based clustering methods applyed to amenity data. The API currently supports only two clustering methods: DBSCAN and HDBSCAN. The data contains cluster labels, and the polygons in the form of (lat, lng) pairs of their vertex.
In the case of HDBSCAN additional data is provided relating to PTAL, Shannon's entropy index, medium household income and population. The parameters needed are as follows:
- cType: the type of clustering method that defines the polygons, it can take the values of DBSCAN2 and HDBSCAN