928 research outputs found
Novel evaluation metrics for sparse spatio-temporal point process hotspot predictions - a crime case study
Many physical and sociological processes are represented as discrete events in time and space. These spatio-temporal point processes are often sparse, meaning that they cannot be aggregated and treated with conventional regression models. Models based on the point process framework may be employed instead for prediction purposes. Evaluating the predictive performance of these models poses a unique challenge, as the same sparseness prevents the use of popular measures such as the root mean squared error. Statistical likelihood is a valid alternative, but this does not measure absolute performance and is therefore difficult for practitioners and researchers to interpret. Motivated by this limitation, we develop a practical toolkit of evaluation metrics for spatio-temporal point process predictions. The metrics are based around the concept of hotspots, which represent areas of high point density. In addition to measuring predictive accuracy, our evaluation toolkit considers broader aspects of predictive performance, including a characterisation of the spatial and temporal distributions of predicted hotspots and a comparison of the complementarity of different prediction methods. We demonstrate the application of our evaluation metrics using a case study of crime prediction, comparing four varied prediction methods using crime data from two different locations and multiple crime types. The results highlight a previously unseen interplay between predictive accuracy and spatio-temporal dispersion of predicted hotspots. The new evaluation framework may be applied to compare multiple prediction methods in a variety of scenarios, yielding valuable new insight into the predictive performance of point process-based prediction
Graph deep learning model for network-based predictive hotspot mapping of sparse spatio-temporal events
The predictive hotspot mapping of sparse spatio-temporal events (e.g., crime and traffic accidents) aims to forecast areas or locations with higher average risk of event occurrence, which is important to offer insight for preventative strategies. Although a network-based structure can better capture the micro-level variation of spatio-temporal events, existing deep learning methods of sparse events forecasting are either based on area or grid units due to the data sparsity in both space and time, and the complex network topology. To overcome these challenges, this paper develops the first deep learning (DL) model for network-based predictive mapping of sparse spatio-temporal events. Leveraging a graph-based representation of the network-structured data, a gated localised diffusion network (GLDNet) is introduced, which integrating a gated network to model the temporal propagation and a novel localised diffusion network to model the spatial propagation confined by the network topology. To deal with the sparsity issue, we reformulate the research problem as an imbalance regression task and employ a weighted loss function to train the DL model. The framework is validated on a crime forecasting case of South Chicago, USA, which outperforms the state-of-the-art benchmark by 12% and 25% in terms of the mean hit rate at 10% and 20% coverage level, respectively
Leveraging Mobility Flows from Location Technology Platforms to Test Crime Pattern Theory in Large Cities
Crime has been previously explained by social characteristics of the
residential population and, as stipulated by crime pattern theory, might also
be linked to human movements of non-residential visitors. Yet a full empirical
validation of the latter is lacking. The prime reason is that prior studies are
limited to aggregated statistics of human visitors rather than mobility flows
and, because of that, neglect the temporal dynamics of individual human
movements. As a remedy, we provide the first work which studies the ability of
granular human mobility in describing and predicting crime concentrations at an
hourly scale. For this purpose, we propose the use of data from location
technology platforms. This type of data allows us to trace individual
transitions and, therefore, we succeed in distinguishing different mobility
flows that (i) are incoming or outgoing from a neighborhood, (ii) remain within
it, or (iii) refer to transitions where people only pass through the
neighborhood. Our evaluation infers mobility flows by leveraging an anonymized
dataset from Foursquare that includes almost 14.8 million consecutive check-ins
in three major U.S. cities. According to our empirical results, mobility flows
are significantly and positively linked to crime. These findings advance our
theoretical understanding, as they provide confirmatory evidence for crime
pattern theory. Furthermore, our novel use of digital location services data
proves to be an effective tool for crime forecasting. It also offers
unprecedented granularity when studying the connection between human mobility
and crime
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