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12 International Conference on Computational Science and Its Applications
th
(ICCSA2012 in Salvador da Bahia/Brazil)
Session GeoAnMod-3 on Monday June 18, 2012
Geovisualization and Geostatistics:
A Concept for the Numerical and Visual
Analysis of Geographic Mass Data
Julia Gonschorek | Geoinformation Research Group | University of Potsdam
Co-Author: Lucia Tyrallová | Geoinformation Research Group | University of Potsdam

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Outline
1. Preface and Motivation
2. Spatio-Temporal Analysis for Civil Security
3. Summary and Future Plans
@gonschorek ∙ university of potsdam

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1. Preface and Motivation (1)
 Increasing availability of (mass-) data and need for specific
information  complex computational analysis tools and techniques
 Highly dimensional data needs to be analysed rapidly to discover
relationships, clusters and trends
 Scientific visualization offers a wide range of methods and
techniques to efficiently analyze and visualize spatial and temporal
data and information
@gonschorek ∙ university of potsdam

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1. Preface and Motivation (2)
@gonschorek ∙ university of potsdam

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2. Spatio-Temporal Analysis for Civil Security (1)
Simple lineplot to visualize the temporal distribution of internistic emergencies
in the City of Cologne (total number of 26,475 in 2007):
@gonschorek ∙ university of potsdam

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2. Spatio-Temporal Analysis for Civil Security (2)
Box-and-Whisker-Plots to show differences in varaiances:
R-Code (without months “January” and “February”):
emergency <- read.csv(“c:tempintern07.csv”,header=T, sep=“;”)
chisq.test(emergency[1:7,4:13])
Pearson's Chi-squared test
data: emergency[1:7, 4:13]
X-squared = 409.496, df = 54, p-value < 2.2e-16
qchisq(0.95,54)
72.15322
The non-parametric χ²- Test for testing dependency validates the observation.
The correlation between “Day of the Week” and “Months” is highly significant.
@gonschorek ∙ university of potsdam

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2. Spatio-Temporal Analysis for Civil Security (3)
Heatmap to detect temporal clusters:
R-Code:
install.packages(“gplots”)
install.packages
(“RColorBrewer”)
library(gplots)
library(RColorBrewer)
x <-
read.csv(“c:temp
intern07.csv”, header=T,
sep=“;”, row.names=1)
matrix=data.matrix(x)
heatmap.2(matrix, Rowv=T,
Colv=T, dendrogram=c(“none”),
distfun=dist,
hclustfun=hclust, key=T,
keysize=1, trace=“none”,
density.info=c(“none”),
margins=c(10,10),
col=brewer.pal(10,”PiYG”))
@gonschorek ∙ university of potsdam

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2. Spatio-Temporal Analysis for Civil Security (4)
Map of 17,000 surgery emergencies in the City of Cologne during July, 2007 and
June 2008 with kernel density estimation using Epanechnikov kernel:
Hotspots of surgery emergencies…
on Friday on Saturday on Sunday
@gonschorek ∙ university of potsdam

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2. Spatio-Temporal Analysis for Civil Security (5)
(e)
(d)
Surgical Emergencies
> total: 300
(c)
> daytime: 6.00 – 7.00 a.m.
> year: 2009
> district: Altstadt Sued
(b)
(a)
(a) Data Source: all incoming emergency calls
(b) First-order circle: inhomogeneous parts for cluster or administrative information (urban districts)
(c) Second-order circle: homogeneous parts for temporal information: year
(d) Third-order circle: inhomogeneous parts for temporal information: month, daytime, …
@gonschorek ∙ university of potsdam
(e) Fourth-order circle: inhomogeneous parts; Type of emergency case

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3. Summary and Future Plans (1)
 Methods can be used to efficiently extract spatio-temporal
information from large databases
 It is shown how specific emergency services cluster in space
and time
 Nearly the whole city area of Cologne was an emergency
scene during the analysed perid. Especially the city centre,
leisure facilities and nursing homes were emergency hot spots
 Combination of different explorative techniques with those
from geovisualisation can check the (long-term) experience of
the firefighters on different spatial scales and precision
@gonschorek ∙ university of potsdam

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3. Summary and Future Plans (2)
 Methods and results are important for future explorative
analyses and geovisual analytics
 Information and a deep understanding of specific
distributions and patterns as well as (ir-) regularities of
intensity are absolutely necessary for prevention
measurements to be well-directed and needs based.
 Time-series-analysis and prognoses are suitable for the
operational, strategic and tactical planning.
@gonschorek ∙ university of potsdam

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Thank you for your attention!
Julia Gonschorek | julia.gonschorek@uni-potsdam.de
Department of Geography | University of Potsdam
http://www.geographie.uni-potsdam.de/geoinformatik