On Discovering Co-Location Patterns in Datasets: A Case Study of Pollutants and Child Cancers

TL;DR

This paper presents a novel grid-based co-location pattern mining approach that accounts for uncertainty and is suitable for datasets with extended spatial objects, applied to pollutants and childhood cancer data.

Contribution

It introduces a grid transactionization method for co-location mining that handles uncertainty and extended spatial objects, advancing spatial data analysis techniques.

Findings

Effective detection of significant co-location patterns in synthetic datasets.

Successful application to real pollutant and childhood cancer datasets.

Provides a framework for spatial association analysis between environmental and health data.

Abstract

We intend to identify relationships between cancer cases and pollutant emissions and attempt to understand whether cancer in children is typically located together with some specific chemical combinations or is independent. Co-location pattern analysis seems to be the appropriate investigation to perform. Co-location mining is one of the tasks of spatial data mining which focuses on the detection of co-location patterns, the sets of spatial features frequently located in close proximity of each other. Most previous works are based on transaction-free apriori-like algorithms which are dependent on user-defined thresholds and are designed for boolean data points. Due to the absence of a clear notion of transactions, it is nontrivial to use association rule mining techniques to tackle the co-location mining problem. The approach we propose is based on a grid "transactionization" of the geographic space and is designed to mine datasets with extended spatial objects. Uncertainty of the feature presence in transactions is taken into account in our model. The statistical test is used instead of global thresholds to detect significant co-location patterns and rules. We evaluate our approach on synthetic and real datasets. This approach can be used by researchers looking for spatial associations between environmental and health factors. In addition, we explain the data modelling framework which is used on real datasets of pollutants (PRTR/NPRI) and childhood cancer cases.