Exploring Spatiotemporal Phenological Patterns and Trajectories Using Self-Organizing Maps

The Cartographic Journal

Kraak

2013

Analysing spatio-temporal weather patterns is fundamental to better understand the system Earth. Such patterns depend on the spatial and temporal resolution of the available data. Here, we study a particular spatio-temporal pattern, namely, synchronisation, and how this is affected by different temporal resolutions and temporal heterogeneity. Twenty years of daily temperature data collected in 28 Dutch meteorological stations are used as case study. Given the complexity of the analysis, we propose a geovisual analytic approach based on self-organizing maps (SOMs). This approach allows exploring the data from two perspectives: (1) station-based, in which spatially synchronous weather stations are grouped into clusters; and (2) year-based, in which temporal synchronisation is analysed using a calendar year as basic unit and similar years are clustered. Clusters are identified using the SOM U-matrices and maps. Next, the spatial distribution of synchronous stations is displayed in the geographic space. Trend plots are used to illustrate trends in every cluster and the temperatures of stations and years are compared with the corresponding cluster representative values to identify anomalies in the temperature records. The analysis is repeated at daily, weekly and monthly resolutions to study the effects of different temporal resolutions on synchronisation. Also daily spatial synchronisation results for all years with those for groups of daily synchronous years are analysed to study the effects of temporal heterogeneity. Results show that synchronisation results are different at different temporal resolutions. Monthly results are the most stable ones both in station-based and year-based. It is also observed that spatial synchronisation results are simplified when considering temporal heterogeneity.

Section: Discovering Synchronisation In Time Series Datamentioning

confidence: 74%

Visual Discovery of Synchronisation in Weather Data at Multiple Temporal Resolutions

The Cartographic Journal

Kraak

2013

“…Although in the past phenological observations were collected by individual observers at relatively small scales, developments in information and communication technologies are presenting new opportunities for overcoming these historical limitations. Today phenological data collection is "crowd-sourced" to the general public (Vliet et al, 2003;Schwartz et al, 2012), and observed from space (e.g., White et al, 2009;Zurita-Milla et al, 2013). As these networks of volunteers and remote sensing operations grow in scale and scope, interpretation of the sheer volume of data will be greatly improved if it can be readily compared with clearly defined markers of seasonal evolution.…”

Section: Introductionmentioning

confidence: 99%

A Matlab© toolbox for calculating spring indices from daily meteorological data

Ault

Computers & Geosciences

Schwartz

2015

a b s t r a c tMetrics to track seasonal transitions are needed for a wide variety of ecological and climatological applications. Here a MATLAB©toolkit for calculating spring indices is documented. The spring indices have been widely used in earlier studies to model phenological variability and change through time across a wide range of spatial scales. These indices require only daily minimum and maximum temperature observations (e.g., from meteorological records) as input along with latitude, and produce a day of year value corresponding to the simulated average timing of first leaf and first bloom events among three plant cultivars. Core functions to calculate the spring indices require no external dependencies, and data for running several illustrative test cases are included. Instructions and routines for conducing more sophisticated monitoring and modeling studies using the spring indices are also supplied and documented.

“…The method of combining the analysis of synoptic states and Sammon’s trajectories has been previously used by Zurita-Milla et al [27]. Yet, here we applied it in combination with mapping back subsets on point data.…”

Section: Methodsmentioning

confidence: 99%

Self-organizing maps as an approach to exploring spatiotemporal diffusion patterns

Augustijn

2013

Int J Health Geogr

BackgroundSelf-organizing maps (SOMs) have now been applied for a number of years to identify patterns in large datasets; yet, their application in the spatiotemporal domain has been lagging. Here, we demonstrate how spatialtemporal disease diffusion patterns can be analysed using SOMs and Sammon’s projection.MethodsSOMs were applied to identify synchrony between spatial locations, to group epidemic waves based on similarity of diffusion pattern and to construct sequence of maps of synoptic states. The Sammon’s projection was used to created diffusion trajectories from the SOM output. These methods were demonstrated with a dataset that reports Measles outbreaks that took place in Iceland in the period 1946–1970. The dataset reports the number of Measles cases per month in 50 medical districts.ResultsBoth stable and incidental synchronisation between medical districts were identified as well as two distinct groups of epidemic waves, a uniformly structured fast developing group and a multiform slow developing group. Diffusion trajectories for the fast developing group indicate a typical diffusion pattern from Reykjavik to the northern and eastern parts of the island. For the other group, diffusion trajectories are heterogeneous, deviating from the Reykjavik pattern.ConclusionsThis study demonstrates the applicability of SOMs (combined with Sammon’s Projection and GIS) in spatiotemporal diffusion analyses. It shows how to visualise diffusion patterns to identify (dis)similarity between individual waves and between individual waves and an overall time-series performing integrated analysis of synchrony and diffusion trajectories.