From: Subject: dgOnline NEWS > Application of Social Network Analysis - Harvard DG researcher features tutorial at dg.o 2006 Date: Wed, 11 Apr 2007 13:37:29 -0400 MIME-Version: 1.0 Content-Type: multipart/related; type="text/html"; boundary="----=_NextPart_000_0046_01C77C3E.8C9FD4D0" X-MimeOLE: Produced By Microsoft MimeOLE V6.00.2900.3028 This is a multi-part message in MIME format. ------=_NextPart_000_0046_01C77C3E.8C9FD4D0 Content-Type: text/html; charset="iso-8859-1" Content-Transfer-Encoding: quoted-printable Content-Location: http://www.digitalgovernment.org/news/stories/2006/0306/0306_patil_heyman.jsp dgOnline NEWS > Application of Social Network = Analysis - Harvard DG researcher features tutorial at dg.o 2006
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GeoInformatics: Detecting "Hotspots" to Prevent Crisis=20 Situations
Penn State, = Distinguished=20 Professor features workshop at dg.o=20 2006
By Karen=20 Heyman
For the DGRC

Project Geoinformatic = Surveillance:=20
 


Researcher profile: G.P=20 Patil

Project profile:Project=20 Geoinformatic Surveillance: Hotspot Detection and = Prioritization=20 Across Geographic Regions and Networks for Digital = Government in the=20 21st Century



At this year=92s 7th Annual International = Conference on=20 Digital Government Research, there will be an all-day workshop on = =93Digital= =20 Governance and Hotspot GeoInformatics for Monitoring, Etiology, = Early=20 Warning, and Sustainable Management.=94 Taking place on = Sunday, May 21,=20 2006 in San Diego, it will be conducted by Ganapati P. Patil,=20 Distinguished Professor and Director, Penn State Center for = Statistical=20 Ecology and Environmental Statistics.

Geoinformatics is a relatively new discipline = that uses=20 the tools of computer science to analyze large sets of = geographically=20 based data. It is especially relevant to practitioners in public = health=20 and public policy for issues ranging from persistent poverty to = epidemics=20 and the aftermath of natural disasters. It is also valuable for = those in=20 varied environmental management disciplines, from conservation to = invasive=20 species management. Additionally, it can provide critical = information for=20 security professionals for object recognition and tracking, and = for=20 potential spread of the effects of bioterror weapons.

The workshop will cover geoinformatic models and = tools=20 appropriate for detecting and prioritizing =93hotspots.=94  = Patil and his=20 colleagues have been working on how to identify potential = "hotspots."=20 These can be crisis points in the military sense involving areas = and=20 networks of robots, sensors, or wireless devices, but also in the=20 environmental and human health sense involving events of societal=20 importance over geographic regions or across networks.

To take a public health example, a =93hotspot=94 = could refer=20 to where a disease outbreak has started, where it is at its worst, = and=20 most importantly, where and when one may emerge. A public policy = analyst=20 might try to detect a hotspot of poverty, looking at both = geography and=20 temporal dynamics, as Patil explains, =93The questions to ask are, = =91What are=20 the poverty patches in the city, how have they changed over time, = have the=20 patches been growing, spreading, merging, shifting? Are there any = poverty=20 patch trajectories that can be useful for making policy for = poverty=20 alleviation=92?=94

The identification process for these = hotspots=20 requires the coordination of many spatial and temporal = parameters. =20 In order to correctly incorporate and interpret all of these = parameters,=20 Patil and his collaborators start with the spatial scan statistic, = a=20 widely used metric in public health, and modify it to apply to=20 environmental sciences and elsewhere.

"The popular = health-area scan=20 statistic is a statistical method designed to detect a local = excess of=20 events and to test if such an excess can reasonably have occurred = by=20 chance," explains Patil. "However, its major limitation is that it = is=20 circle-based. The clusters can be of any shape, and cannot be = captured=20 only by circles. In more general settings, this is likely to give = more=20 false alarms and more of a false sense of security. What we need = is the=20 capability to detect arbitrarily shaped hotspots. We plan to = accomplish=20 this using our innovation with upper level sets and their = connected=20 components extended to permutation based upper level sets (PULSE), = and=20 also using novel genetic algorithms." 

The benefit of hotspot detection is that it = allows for=20 customized analysis and thus the ability to create more specific, = targeted=20 policies. =93One can see that while 400 cities don=92t need 400 = policies, they=20 cannot do with one single policy, one size fits all,=94 says = Patil,=20 =93Unfortunately, that has been the case so far in most countries = when it=20 comes to poverty alleviation, and by and large the policies have=20 failed.=94

Detection is only the first step, equally = important is=20 prioritizing.  =93Once you identify hotspots, you want to = know which=20 hotspots need monitoring, which ones can be used for etiological = analysis,=20 which ones are ready for some assessment and management, so you = want to=20 prioritize and rank these hotspots.=94  Of course, as = practitioners=20 know, there are different stakeholders in every situation. With = each=20 stakeholder providing a score for each hotspot, =93You don=92t = want to=20 construct an index, because it=92s a question of apples and = oranges,=94 says=20 Patil, =93You want to prioritize and rank these hotspots without = crunching=20 these multiple indicators into a single index, so the question is = how this=20 can be done.=94

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=95 dg.o 2006 = Convenes May=20 21-24, 2006  
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=95 dg.o 2006 features = Workshops=20 on:
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   GeoInformatics
=95=20 dg.o 2006 features Tutorial on:
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=95 eChallenges=20 e-2006 Issues CFP
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Con= tribute=20 to dgOnline

A conventional solution is to assign a composite = numerical=20 score to each hotspot by combining the indicator information in = some=20 fashion. But warns Patil, =93Consciously or otherwise, every such = composite=20 involves judgments, often arbitrary or controversial about = tradeoffs or=20 substitutability among indicators.=94

In Patil=92s methodology, the solution is, = =93Rather than=20 trying to combine indicators, we take the view that the relative = positions=20 in indicator space determine only a partial ordering and that a = given pair=20 of hotspots may not be inherently comparable. Working with Hasse = diagrams=20 of the partial order, we study the collection of all rankings that = are=20 compatible with the partial order. And using our innovation with = partial=20 order sets, rank frequency distributions, and cumulative rank = frequency=20 operators, we accomplish the desired prioritization of the = hotspots.=94

In essence, it is a problem in multi-dimensional = space.=20 You can do an x, y plot to measure say, income by age, but suppose = you=20 have two hundred countries and you want to rank them according to=20 human/environmental interface progress, which includes several = indicators.=20 The Hasse diagram allows you to collapse several dimensions into = levels=20 that can be compared against each other, and provides a basis for=20 insightful analysis leading to a broadly acceptable ranking.

Says Patil, =93We will share these and other = methods and=20 tools with the workshop in the hopes that the participants will be = able to=20 use some of these methods and tools for analyzing their own = datasets and=20 databases if they=92re looking for hotspots and prioritizing = them.=94

WORKSHOP STRUCTURE

The workshop will be divided into morning and = afternoon=20 sessions, with the morning devoted to the toolbox exposition with = examples=20 and datasets, with particular emphasis on public health, crime, = ecohealth,=20 poverty, networks and the like. In the afternoon, there will be = actual=20 case studies presented by people working around the world. =93One = panel=20 participant is an Indian New Delhi official in the Department of=20 Information Technology, who is a senior director of the = e-government=20 program,=94 says Patil. =93An innovative young expert from Brazil = is going to=20 present a hotspot case study on dengue fever and malaria in = Brazil, using=20 creative genetic algorithms together with environmentally = meaningful=20 adjacency definitions.  My colleague from Penn State will = make a case=20 study on crop disease and the management advisory on whether a = farmer=20 should spray or not spray.  A vice-president of the famous = Bogor=20 Agricultural University in Indonesia is going to speak about what=20 Indonesia is going to do to implement hotspot = geoinformatics.=94

Patil welcomes participants to submit their own = case=20 studies for presentation: =93It will be a good workshop for people = to=20 attend, and speak, and share some of their issues and = datasets.=94 =20 Participants are also welcome just to watch, hear, and overhear = the=20 exciting developments and applications for this century.

Links:

http://www.stat.psu.edu/hotspot= s

http://en.wikipedia.o= rg/wiki/Hasse_diagram

 

 


  3D"Digital This site is maintained by = the Digital Government Research = Center at the=20 University of Southern California's Information Sciences Institute. = 3D"USC =95 CONTACT=20 =95 POLICIES=20  
 

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