Neural network systems and methods for event parameter determination
Systems, methods, and computer program products are provided for determining an event parameter are provided. Event data can be matched to a grid comprising gridlines and cells defined by the gridlines. The grid can be mapped to a predetermined area. Each cell can comprise a number of events per predetermined time interval. The cells can be sorted into classes based on the number of events occurring during the predetermined time interval to produce a classified data set. Features can be extracted from the classified data set. The extracted features can be processed using a classifier to determine the event parameter for a future time interval in at least one cell of the cells, for example, crime events. The classifier can comprise a neural network. Systems can comprise one or more of a processor, a neural network, and a user interface.
1. A neural network system, the system comprising:
a processor configured to:
match event data, the event data comprising event location data and event time data, to a grid comprising gridlines and cells defined by the gridlines, wherein the grid is mapped to a predetermined area, and each cell comprises a number of events per predetermined time interval,
sort the cells into classes based on the number of events occurring during the predetermined time interval in each cell to produce a classified data set, wherein the classes are percentiles or weighted percentiles, and
extracting features from the classified data set;
a neural network configured to determine an event parameter for a future time interval in at least one cell of the cells; and
a user interface configured to enable a user to operate the neural network and the processor, and configured to display the event parameter on a map comprising the grid and the cell.
2. The system of claim 1 , wherein the features comprise a temporal feature, a spatial feature, or a spread feature, or any combination thereof.
3. The system of claim 1 , wherein the extracting comprises use of density-based spatial clustering of applications with noise (DBSCAN).
4. The system of claim 1 , wherein the neural network comprises a convolutional long short-term memory (Conv-LSTM) neural network.
5. The system of claim 1 , wherein the event comprises a crime event, sales event, a taxable event, an epidemiological event, a meteorological event, a vehicular traffic event, an internet traffic event, a utility consumption event, or a political event, or any combination thereof.