1. Hot zone analysis
This task will need to perform a range join operation on a rectangle datasets and a point
dataset. For each rectangle, the number of points located within the rectangle will be obtained.
The hotter rectangle means that it includes more points. So this task is to calculate the hotness
of all the rectangles.
2. Hot cell analysis
This task will focus on applying spatial statistics to spatio-temporal big data in order to identify
statistically significant spatial hot spots using Apache Spark. The topic of this task is from ACM
SIGSPATIAL GISCUP 2016.
The Problem Definition page is here: [login to view URL]
The Submit Format page is here: [login to view URL]
Special requirement (different from GIS CUP)
As stated in the Problem Definition page, in this task, you are asked to implement a Spark
program to calculate the Getis-Ord statistic of NYC Taxi Trip datasets. We call it "Hot cell