Spatiotemporal change footprint pattern discovery: an inter‐disciplinary survey

Abstract

Given a definition of change and a dataset about spatiotemporal (ST) phenomena, ST change footprint discovery is the process of identifying the location and/or time of such changes from the dataset. Change footprint discovery is fundamentally important for the study of climate change, the tracking of disease, and many other applications. Methods for detecting change footprints have emerged from a diverse set of research areas, ranging from time series analysis and remote sensing to spatial statistics. Researchers have much to learn from one another, but are stymied by inconsistent use of terminology and varied definitions of change across disciplines. Existing reviews focus on discovery methods for only one or a few types of change footprints (e.g., point change in a time series). To facilitate sharing of insights across disciplines, we conducted a multi-disciplinary review of ST change patterns and their respective discovery methods. We developed a taxonomy of possible ST change footprints and classified our review findings accordingly. This exercise allowed us to identify gaps in the research that we consider ripe for exploration, most notably change pattern discovery in vector ST datasets. In addition, we illustrate how such pattern discovery might proceed using two case studies from historical GIS.

Spatiotemporal Data Mining: A Computational Perspective

“…directional: above, below, northeastern • others: shape based and visibility [48][49][50][51][52][53] field model regular irregular 1. cubic map algebra [54] 2. temporal correlation, tele-connection local, focal, zonal change across snapshots [29] cellular automation [55] spatial network graph 1. predecessor/successor on a Lagrangian path 2. temporal centrality [56] 3. network flow [57][58][59] change in centrality, connectivity spatiotemporal coupling of network events One way to deal with implicit spatiotemporal relationships is to materialize the relationships into traditional data input columns and then apply classical data mining techniques [60][61][62][63][64]. However, the materialization can result in loss of information [23].…”

“…This section follows the taxonomy of spatiotemporal change footprint patterns as proposed in [29]. In this taxonomy, spatiotemporal change footprints are classified along two dimensions: temporal and spatial.…”

“…Among the surveys without focuses on statistical foundation, Koperski et al [24] and Ester et al [25] reviewed spatial data mining from a spatial database approach; Roddick et al [12] provided a bibliography for spatial, temporal and spatiotemporal data mining; Miller et al [26] cover a list of recent spatial and spatiotemporal data mining topics but without a systematic view of statistical foundation. Among surveys covering statistical foundations, Shekhar et al 2003 [23] reviewed several spatial pattern families focusing on spatial data's unique characteristics; Kisilevich et al [27] reviewed spatiotemporal clustering research; Aggarwal et al [28] has a chapter summarizing spatial and spatiotemporal outlier detection techniques; Zhou et al [29] reviewed spatial and spatiotemporal change detection research from an interdisciplinary view; Cheng et al 2014 [30] reviews state of the art spatiotemporal data mining research including spatiotemporal autocorrelation, space-time forecasting and prediction, space-time clustering, as well as space-time visualization; Shekhar et al 2011 [31] give the most recent review of spatial data mining research. However, its discussion of spatiotemporal patterns is limited (e.g., nothing on spatiotemporal change patterns or statistically significant hotspots).…”

Landscape Pattern and Ecological Security Assessment and Prediction Using Remote Sensing Approach

“…The wildly accepted ecological security assessment system include the "ecological footprint" concept and model, the concept of pressure-state-response (PSR) framework model, and the concept of natureeconomy-society framework model [8,9].…”

Mining Persistent and Dynamic Spatio-Temporal Change in Global Climate Data

“…In particular, the biggest question in climate science is to verify and quantify global climate change (Shekhar et al 2015). The analysis of change in climate data can generally be classified into two categories: spatial change and temporal change (Zhou et al 2014). Spatial change is to detect change in space at only one point in time, such as the analysis of air temperature change at different latitudes.…”