This paper adopts Periodica algorithm by enhancing the hotspot detection, which accomplished through substituting Kernel Density Estimate (KDE) with Getis-Ord Gi*. The new method is applied in periodical behavior discovery in animal movement. It is a very typical case in spatial data mining for integrating spatial autocorrelation with traditional statistical analysis. As the author mentioned, KDE is criticized because it assume data points are independent and sensitive to the shape of data points. Getis-Ord Gi* perfectly avoids these drawbacks; however, Getis-Ord Gi* is grid-based and still suffering from Modifiable Areal Unit Problem (MAUP). It means the model will have different results when applied in different spatial scales, which represents the granularity here. Considering the scale problems is a significant different between spatial data mining and traditional datamining. Therefore, it is important to explicitly discuss the size of grid in practices, and it is also to necessary to think about time scales. Periodical behaviors may simultaneously happen in many different-length and interlaced periods (e.g., seasonal behaviors and daily behaviors can happen together). Sometimes there is only one kinds of periods we need to consider (e.g., animal immigration), but sometimes we may need to have cross-scale analysis (e.g., human’s periodic behaviors), which will make the situation far more complicated.

Beyond the Periodica algorithm, I believe there could be a better way to discover the periodic behaviors. First, I think no matter what hotspot detection methods used, it never gets rid of arbitrarily determining the hotspots. More instinctively, it means how big a staying region is to represent a periodic behavior is happening. Second, since the points in trajectories are not independent, why we separate them from trajectories to conduct analysis? Can we directly analyze the trajectories even the basis are still points? Do we only care about the periodical behaviors within certain locations and how the directions they traveled? Is there any interesting periodic behaviors during the travelling (e.g., certain routes they always travel)? Simplifying information to binary data also means losing information for further discovering. I’m not arguing we always have to know all the answers of those questions, but when it is necessary, we should have better methods or tools to do the job. Third, in my perspectives, periodical behaviors mean always having some event in a certain time. There is nothing about space. I think most of literature have their clear definitions about periodical behaviors but seem not natural. Mathematically, it is good to make “hard” definitions for analysis, but we still need more discussion about this assumption (e.g., using locations to situate periodical behaviors). Therefore, I argue we should have better solution to substitute the Periodica algorithms if necessary, and I suggest it can start from the concept of clearly separating space from other information in spatial data mining.