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Geovisualization and Geostatistics:A Concept for the Numerical and

Visual Analysis of Geographic Mass Data

12th International Conference on Computational Science and Its Applications

(ICCSA2012 in Salvador da Bahia/Brazil)

Session GeoAnMod-3 on Monday June 18, 2012

Julia Gonschorek | Geoinformation Research Group | University of PotsdamCo-Author: Lucia Tyrallová | Geoinformation Research Group | University of Potsdam

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Outline

1. Preface and Motivation2. Spatio-Temporal Analysis for Civil Security3. Summary and Future Plans

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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

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ICCSA Brazil 2012

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1. Preface and Motivation (2)

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2. Spatio-Temporal Analysis for Civil Security (1)

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Simple lineplot to visualize the temporal distribution of internistic emergencies in the City of Cologne (total number of 26,475 in 2007):

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2. Spatio-Temporal Analysis for Civil Security (2)

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ICCSA Brazil 2012

Box-and-Whisker-Plots to show differences in varaiances:

The non-parametric χ²- Test for testing dependency validates the observation. The correlation between “Day of the Week” and “Months” is highly significant.

R-Code (without months “January” and “February”):

emergency <- read.csv(“c:\\temp\\intern07.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

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2. Spatio-Temporal Analysis for Civil Security (3)

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ICCSA Brazil 2012

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”))

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2. Spatio-Temporal Analysis for Civil Security (4)

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Hotspots of surgery emergencies…on Friday on Saturday on Sunday

ICCSA Brazil 2012

Map of 17,000 surgery emergencies in the City of Cologne during July, 2007 and June 2008 with kernel density estimation using Epanechnikov kernel:

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2. Spatio-Temporal Analysis for Civil Security (5)

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ICCSA Brazil 2012

(a)

(b)

(c)

(d)

Surgical Emergencies > total: 300 > daytime: 6.00 – 7.00 a.m. > year: 2009 > district: Altstadt Sued

(e)

(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, … (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

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ICCSA Brazil 2012

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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.

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Thank you for your attention!

Julia Gonschorek | julia.gonschorek@uni-potsdam.deDepartment of Geography | University of Potsdamhttp://www.geographie.uni-potsdam.de/geoinformatik