Spatial Data Science

Spatial Big Data: Platforms, Analytics, and Science

Emerging non-traditional spatial datasets from geo-social media, sensor networks, and volunteers are important due to societal applications such as situation assessment after natural disasters, monitoring urban traffic, etc. However, such datasets, called spatial big data, often exceed the capacity of commonly used spatial computing platforms. Spatial big data presents new challenges for their ...

Rethinking Spatial Processing in Data-Intensive Science

In this paper we address the problem of supporting data-driven science in geo-scientific application domains where scientists need to filter, analyze, and correlate large data sets in an interactive manner. We identify ten fundamental requirements for such a new type of processing system. They span from supporting spatial data types over using workflows for data lineage, to fast and flexible co...

Bayesian Prediction in Spatial Data Analysis

Spatial Modeling of Censored Survival Data

An important issue in survival data analysis is the identification of risk factors. Some of these factors are identifiable and explainable by presence of some covariates in the Cox proportional hazard model, while the others are unidentifiable or even immeasurable. Spatial correlation of censored survival data is one of these sources that are rarely considered in the literatures. In this paper,...

Science and data science.

Data science has attracted a lot of attention, promising to turn vast amounts of data into useful predictions and insights. In this article, we ask why scientists should care about data science. To answer, we discuss data science from three perspectives: statistical, computational, and human. Although each of the three is a critical component of data science, we argue that the effective combina...