  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.
