Download NCHS C - Sites at Penn State

This report is very disappointing. What
kind of software are you using?
Space Age and
Stone Age Syndrome
• Data:
• Analysis:
Data
Analysis
Space Age
Stone Age
Space Age/Stone Age
Space Age/Stone Age
Space Age
Stone Age
+
+
+
The Value of Mapping
Maps provide an efficient and unique method of
demonstrating distributions of phenomena in space.
Though [maps are] constructed primarily to show
facts, to show spatial distributions with an accuracy
which cannot be attained in pages of description or
statistics, their prime importance is as research tools.
They record observations in succinct form; they aid
analysis; they stimulate ideas and aid in the
formation of working hypotheses; they make it
possible to communicate findings; they assist in
research and policy research.
Disease Mapping
•Disease Mapping is about the use and interpretation of maps
showing the incidence or prevalence of disease.
•Disease data occur either as individual cases or as groups (or
counts) of cases within census tracts.
•Any disease map must be considered with the appropriate
background population which gives rise to the incidence.
•Maps answer the question: where? They can reveal spatial patterns
not easily recognized from lists of statistical data.
•Maps showing infectious diseases can help elucidate the cause of
disease. Maps showing non-infectious diseases may be used to
generate hypotheses of disease causation.
National Mortality Maps and
Health Statistics
• Health Service Areas, Counties, Zip Codes, …
• Geographical Patterns for Health Resource
Allocation
• Study Areas for Putative Sources of Health Hazard
– Balance between dilution effect and edge effect
• Case Event Analysis and Ecological Analysis
– Thresholds, contours, corresponding data
• Regional Comparisons and Rankings with
Multiple Indicators/Criteria
• Choices of Reference/Control Areas
Baltimore Asthma Project
Interdisciplinary Analysis of Childhood Asthma in Baltimore, MD
The impact of asthma is escalating within the U.S. and children are particularly impacted with
hospitalization increasing 74% since 1979. This study is investigating climate and environmental
links to asthma in Baltimore, Maryland, a city in the top quintile for children’s asthma in the U.S.
Aerosol Size
Inner Harbor
1. Collect and integrate in-situ measurements, remotely
sensed measurements and clinical records that have
possible relationships to the occurrence of asthma in the
Baltimore, Maryland region.
2. Identify key trigger variables from the data to predict
asthma occurrence on a spatial and temporal basis.
3. Organize a multidisciplinary team to assist in model
design, analysis and interpretation of model results.
4. Develop tools for integrating, accessing and manipulating
relevant health and remote sensing data and make these
tools available to the scientific and health communities.
Time
Partners:
•
•
•
•
•
•
Baltimore City Health Department
Baltimore City School System
Baltimore City Planning Council, Mayor’s Office
State of Maryland Department of the Environment
State of Maryland Department of Health and Human Services
University of Maryland
Asthma Assessment from GIS techniques
Urban Heat Islands
• Use of aircraft and spacecraft remote
sensing data on a local scale to help
quantify and map urban sprawl, land use
change, urban heat island, air quality, and
their impact on human health
• (e.g. pediatric asthma)
Infectious Diseases
• Use of remote sensing data and other available
geospatial data on a continental scale to help
evaluate landscape characteristics that may be
precursors for vector-borne diseases leading to
early warning systems involving landscape health,
ecosystem health, and human health
• Water-Borne Diseases
• Air-Borne Diseases
• Emerging Infectious Diseases
Mekong Malaria and Filariasis Projects
•
•
To develop a predictive model for
assessing risk areas of malaria
transmission in the Greater Mekong Subregion, and
To make risk maps for filariasis
 map breeding sites for major vector
species
 explore the linkage between vector
population density and disease
transmission intensity with
environmental variables
Anticipated Benefits
– Reduce malaria and filariasis
transmission rates
– Minimize environmental damage by
strategically using larvicides and
insecticides
– Improve the health status and
economic activity of populations
affected by malaria and filariasis in
the Greater Mekong Sub-region
Source: Southeast Asian
Journal of Tropical Medicine
and Public Health, volume 30
supplement 4, 1999.
• Quantities of African dust transported
by winds across the Atlantic have
been increasing due to prolonged and
agricultural practices in North Africa
• Recent studies show dust carries
microbes and pollutants that have
been detected in the US and
Caribbean Islands
• Objectives of new studies are to
determine harmful effects, e.g.,
childhood asthma in Puerto Rico
African Dust
Vector-Borne Disease Detection Using NASA Satellite Data
Using near real-time climate data and satellite
imagery, scientists have discovered environmental
triggers for Rift Valley Fever and other diseases
Prediction of Rift Valley Fever outbreaks may be
made up to 5 months in advance in Africa
NDVI anomaly patterns over Africa
during the 1997/98 ENSO warm event
• Research program on the relationships
between environmental parameters (e.g
vegetation), climate ( e.g. rainfall) and
outbreaks of diseases such as:
• Rift Valley Fever (RVF)
• St. Louis Encephalitis Fever (EHF)
• Dengue Fever
• Ebola Fever
• Hanta Virus and others
BENEFITS
• Map and monitor Eco-climatic patterns
associated with disease outbreaks from
satellite platforms
• Better understanding the dynamics of
climate-disease interactions
• Advance warning of disease outbreaks
would enable preventive measures
(vaccination, vector control, etc.) to be
undertaken
• Provide disease surveillance tools to public
health authorities
A New NASA Initiative...
• To apply Space-based
capabilities to examine
environmental conditions
that affect human health
• To enable easy use of and
timely access to Earth
science data and models
• To help our health
community partners to
develop practical early
warning systems
Statistical Ecology, Environmental
Statistics, Health Statistics—1
Sampling, Monitoring, and
Observational Economy Initiatives
— Twentieth Century—
•
•
•
•
•
Capture-Mark-Recapture
Composite, Ranked Set
Adaptive with Clusters and Networks
Transect, Selection Bias, Meta-Analysis
Partnerships:
Statistical Ecology, Environmental
Statistics, Health Statistics—2
Multiscale Advanced Raster Map Analysis
System Initiative—1
• Geospatial Patterns and Pattern Metrics
– Landscape patterns, disease patterns, mortality
patterns
• Surface Topology and Spatial Structure
– Hotspots, outbreaks, critical areas
– Intrinsic hierarchical decomposition, study
areas, reference areas
– Change detection, change analysis, spatial
structure of change
Statistical Ecology, Environmental
Statistics, Health Statistics—3
Multiscale Advanced Raster Map Analysis
System Initiative—2
• Partially Ordered Sets and Hasse Diagrams
– Multiple indicators, comparisons, fuzzy
rankings
– Intrinsic hierarchical groups, reference areas
– Performance measures, composite indices
• System Design and Development
– BAT, BPT, and synergistic collaboration
– Bilateral and multilateral partnerships
National Mortality Maps and Statistics
Geographic Patterns—1
• Mortality rate due to a specific cause of
death
• Elevated rates areas, patterns
• Ordinal thematic maps
• Transition pattern, transitionogram
• Transition matrices; spatial association with
varying distance
• Comparatives with different causes of death
National Mortality Maps and Statistics
Geographic Patterns—2
• Surface topology and spatial structure
• High mortality area delineation
– Hotspots, clusters, outbreaks, corridors
• Surface smoothing
• Masking of true geographic patterns?
• Echelon analysis, original surface,
smoothed surface
National Mortality Maps and Statistics
Relationships—1
• Study areas for response and explanatory
variables relationships
• Response proximity
– Hotspots, thresholds, contours, counter strips
• Spatial proximity
– Buffers, putative hazards
• Dilution effect and edge effect
National Mortality Maps and Statistics
Relationships—2
•
•
•
•
•
•
•
Intrinsic study areas
Intrinsic hierarchical decomposition
Consistent vertical and horizontal balance
Echelons and echelon trees
Urban heat islands and pediatric asthma
Infectious and vector-borne diseases
UV radiation
Multiscale Advanced
Raster Map System
• MARMAP SYSTEM
• Design and Development
• PARTNERSHIP
NSF Digital Government Research Program
Proposal for Invited Re-Submission
MARMAP SYSTEM
• Partnership communication of June 11,
2001.
• NSF Partnership Proposal.
• Review and Response-1
• Review and Response-2
• Review and Response-3
MARMAP SYSTEM
• Partnership Research and Outreach Prospectus:
http://www.stat.psu.edu/~gpp/PDFfiles/prospectus
8-00.pdf
• Our web page for raster map analysis:
http://www.stat.psu.edu/~gpp/newpage11.htm
• Our web page for raster map monographs:
http://www.stat.psu.edu/~gpp/raster.htm
• Our web page for UNEP HEI
http://www.stat.psu.edu/~gpp/unephei.htm
Geospatial Cell-based Data
Kinds of Data
• Cell as a Unit (Regular grid layout)
–
–
–
–
Categorical
Ordinal
Numerical
Multivariate Numerical
• Cell as an Object (Irregular cell sizes and shapes)
–
–
–
–
Partially Ordered
Ordinal
Numerical
Multivariate Numerical
Approaches to Research Issues
Methods and Tools
Data Procurement
and Management
Model-based
Pattern Extraction
Echelons for
Change Detection
Surface Analysis
Visualization Tools
for
Research & Communication
PHASES
for
Data Compression
Model-based Error Maps
for Thematic
Accuracy Assessment
Echelons for
Spatial Complexity
with Multiple Indicators
Software
Design and Development
Data-based Empirical
Pattern Extraction
Sampling Designs
for Thematic
Accuracy Assessment
Partial Ordering Procedures
with Multiple Indicators
Partnership
Synergistics
Landscape Pattern Extraction
Regional Geographic Patterns
Spectral data
Empirical
extraction
Thematic data
Empirical
extraction
Spectral data
Model-based
extraction
Thematic data
Model-based
extraction
Model-based Pattern Extraction
• Pattern = Spatial variability in thematic
maps
• Proposed research limited to raster maps
• Possible Parametric Models:
– Geostatistics (Multi-indicator)
– Markov Random Fields
– Hierarchical Markov Transition Matrix models
(HMTM)
Upper Echelons of Surfaces
Spatial Complexity with
Single Response Variable
Echelons Approach
• Echelon method analyzes cellular data pertaining
to surface variables. Examines changes in
topological connectivity of upper level sets as the
level changes.
• Echelons elucidate spatial structure, help
determine critical areas and corridors, emphasize
areas of complexity, and map various aspects of
surface organization
• Response can be numerical or ordinal
• Cellular tessellation can be regular or irregular
Echelons sDescription—1
Ingredients of an Echelon Analysis:
– Tessellation of a geographic region:
j
c
k
h f d
a b
e
g
a, b, c, … are
cell labels
i
– Response value Z on each cell. Determines a
tessellated (piece-wise constant) surface with Z as
elevation.
• How does connectivity (number of connected
components) of the tessellation change with
elevation?
Echelons Description -- 2
• Think of the tessellated surface as a landform
• Initially the entire surface is under water
• As the water level recedes, more and more of the landform is
exposed
• At each water level, cells are colored as follows:
– Green for previously exposed cells (green = vegetated)
– Yellow for newly exposed cells (yellow = sandy beach)
– Blue for unexposed cells (blue = under water)
• For each newly exposed cell, one of three things happens:
– New island emerges.
Cell is a local maximum. Morse index=2. Connectivity increases.
– Existing island increases in size.
Cell is not a critical point. Connectivity unchanged.
– Two (or more) islands are joined.
Cell is a saddle point Morse index=1. Connectivity decreases.
Echelons Illustrated -- 1
Newly exposed island
j
c
k
Echelon Tree
h f d
a b
e
g
a
g
a
b,c
i
Island grows
j
c
k
h f d
a b
e
i
Echelons Illustrated -- 2
Echelon Tree
Second island appears
j
h f d
a b
e
c
k
g
i
c
k
h f d
a b
e
d
New echelon
Both islands grow
j
a
b,c
i
g
a
b,c
e
d
f,g
Echelons Illustrated -- 3
Islands join – saddle point
j
h f d
a b
e
c
k
a
c
k
d
f,g
e
i
h
New echelon
a
h f d
a b
e
b,c
g
Exposed land grows
j
Echelon Tree
i
g
b,c
e
d
f,g
h
i,j,k
Three echelons
Echelons Illustrated -- 4
•
•
•
•
Each branch in echelon tree determines an echelon
Each echelon consists of cells in the tessellation
The echelons partition the region
Each echelon determines a set of response values Z
(and a corresponding set of values of the explanatory
variables X, if any)
Echelon Partitioning
j
c
k
h f d
a b
e
i
Echelon Tree
g
a
b,c
e
d
f,g
h
i,j,k
Three echelons
Echelons Illustrated – 5
Higher Order Echelons
Receding Waterline
Previous Pictures
Echelon Tree labeled with echelon orders
1
1
1
1
2
2
2 (not 3)
3
1
Echelons Illustrated – 6
Echelon Order Defined
Echelon Tree labeled with echelon orders
1
1
1
1
1
2
2
2 (not 3)
3
• Analogy with stream networks (Horton-Strahler order)
• Leaf branches have order 1
• When two branches of orders p and q join, the new branch has
order:
Max(p, q) if p  q
p+1
if p = q
Echelons Illustrated – 7
Echelon Smoothing
Echelon Tree labeled with echelon orders
Prune ?
1
1
Prune ?
1
1
1
2
2
2 (not 3)
3
• Need for smoothing echelon trees
• Alternative to direct smoothing of surface values
• In complicated echelon trees, root nodes may be most indicative of
noise and become prime candidates for pruning (contraction would
be a better term)
• Criteria for pruning: Echelon relief, Echelon basal area, others?
• What is the corresponding smoothed surface?
Spatial Complexity with
Single Response Variable
Echelons Approach
•
•
•
•
•
•
Issues to be addressed:
Echelon trees and maps
Echelon profiles and other tree metrics
Noise effects and filtering
Comparing echelon trees and maps
Echelon stochastics: surface simulation, tree
simulation, tree metric distributions
Pre-Classification Change Detection
Echelons Approach
• Change vector approach (cell by cell) with
actual spectral data
• Change vector approach (cell by cell) with
compressed (hyperclustered) spectral data
• Pattern-based approach (compressed data
only): Compare segment pattern at time1
with segment pattern at time 2.
Spatial Complexity with
Multiple Indicators Echelons Approach
• Compare echelon features among indicators
for consistency/inconsistency:
– Order
– Number of ancestors (distance from root of
tree)
• Compression by treating features as pseudobands
Geospatial Analysis for
Disease Surveillance—1
Case Event Point Data & Areal Unit Count Data
•
•
•
•
•
Geospatial surveillance
Cluster detection and evaluation
Spatial scan statistics
Choice of zonal parameter space
Candidate zones as circular windows of expanding
size
• Elliptical windows: long island breast cancer
study
• Hyperclusters, echelon trees, upper surface sets
defined by thresholds-based nodes
Geospatial Analysis for
Disease Surveillance—2
Case Event Point Data & Areal Unit Count Data
•
•
•
•
Spatio-temporal surveillance
Cylinders-based spatio-temporal scan statistics
Three-dimensional echelons and echelon trees
Candidate zones as upper surface sets defined by
thresholds-based nodes
• Temporal persistence and patterns
• Cluster alarms, suspect clusters, and their
evaluation
Geospatial and Spatiotemporal
Patterns of Change
•
•
•
•
•
•
•
•
•
•
Multiple cancer mortality statistics and maps
Multiple disease incidence statistics and maps
Across United States over years
Across individual states over years
Pooling over types of cancer/disease
Pooling over types of people
Change detection and change analysis
In space, in time, in space-time
Structure and behavior of chance
Persistence and patterns of elevated areas
Spatial and Spatiotemporal Scan
Statistics—1
SaTScan
• To evaluate reported spatial or spatiotemporal
disease clusters
• To see if they are statistically significant
• To test whether a disease is randomly distributed
• To perform geographical surveillance of disease
• To detect areas of significantly high or low rates
Spatial and Spatiotemporal Scan
Statistics—2
SaTScan
• Poisson model, where the number of events in an
area is Poisson distributed under the null
hypothesis
• Bernoulli model, with 0/1 event data such as cases
and controls
• The program adjusts for the underlying
inhomogeneity of a background population
• With the Poisson model, the program can also
adjust for any number of categorical variates
provided by the user
SaTSCAN – 1
• Goal: Identify geographic zone(s) in which a response
is significantly elevated relative to the rest of a region
• A list of candidate zones Z is specified a priori.
– This list becomes part of the parameter space and the zone
must be estimated from within this list.
– Each candidate zone should generally be spatially connected,
e.g., a union of contiguous spatial units or cells.
– Longer lists of candidate zones are usually preferable
– Expanding circles or ellipses about specified centers are a
common method of generating the list
SaTSCAN – 2
• Example: Infected individuals in a tessellated region G
with cells (spatial units) A
• m(A) = # individuals in cell A (known)
x(A) = # infected individuals in cell A (data)
• Individual infection results from independent Bernoulli
trials
• Full model:
– Bernoulli parameter is p inside zone Z and q  p outside Z
– p, q, Z have unknown parameter values and must be
estimated
• Null model: Bernoulli parameter is constant (but
unknown) throughout the region G
SaTSCAN – 3
• Estimation: Maximum likelihood
Likelihood = L( Z, p, q )
– For fixed Z maximize (analytically) with respect to p
and q giving a partial likelihood L(Z)
– Maximize L(Z) by explicit search through the list of
candidate zones giving the likelihood estimate of Z
SaTSCAN – 4
• Hypothesis Testing: Likelihood ratio statistic
L(Full)
 

L(Null)
L( Zˆ , pˆ , qˆ | Full)
L( pˆ | Null)
– Non-standard situation. Traditional ML theory does
not apply
– Need to determine the null distribution by Monte
Carlo simulation of replicate data sets under the null
model. For each data set Z must be estimated and
the value of the likelihood ratio test statistic
computed
SaTSCAN – 5
• Question: Are there data-driven (rather than a priori) ways of
selecting the list of candidate zones ?
• Motivation for the question: A human being can look at a map
and quickly determine a reasonable set of candidate zones and
eliminate many other zones as obviously uninteresting. Can
the computer do the same thing?
• A data-driven proposal: Candidate zones are the connected
components of the upper level sets of the response surface.
The candidate zones have a tree structure (echelon tree is a
subtree), which may assist in automated detection of multiple,
but geographically separate, elevated zones.
Null distribution: If the list is data-driven (i.e., random), its
variability must be accounted for in the null distribution. A
new list must be developed for each simulated data set.
Multiple Criteria Analysis
Multiple Indicators
Partial Ordering Procedures
• Cells are objects of primary interest, such as countries,
states, watersheds, counties, etc.
• Cell comparisons and rankings are the goals
• Suite of indicators are available on each cell
• Different indicators have different comparative messages,
i.e., partial instead of linear ordering
• Hasse diagrams for visualization of partial orders. Multilevel diagram whose top level of nodes consists of all
maximal elements in the partially ordered set of objects.
Next level consists of all maximal elements when top level
is removed from the partially ordered set, etc. Nodes are
joined by segments when they are immediately
comparable.
Multiple Criteria Analysis
Multiple Indicators
Partial Ordering Procedures
•
•
•
•
Issues to be addressed:
Crisp rankings, interval rankings, fuzzy rankings
Fuzzy comparisons
Echelon analysis of partially ordered sets with ordinal
response levels determined by successive levels in the
Hasse diagram
• Hasse diagram metrics: height, width, dimension,
ambiguity (departure from linear order), etc.
• Hasse diagram stochastics (random structure on the
indicators or random structure on Hasse diagram)
• Hasse diagram comparisons, e.g., compare Hasse diagrams
for different regions
Hasse Diagram
(all countries)
1
2
3
8
9
13
17
22
4
10
45
15
25
26
36
46
6
12
23
28
43
5
48
11
14
18
21
32
27
39
47
7
29
41
50
31
56
20
40
35
51
54
19
38
33
42
52
16
53
60
24
44
55
65
68
76
71
72
34
49
66
69
30
73
37
82
80
114
86
102
88
112
113
57 58 61 62 63 64 67 74 75 77 78 79 83 84 85 93 94 96 98 99 101 104 111 131
59
81
70
89
95
100
97
87
107
90
103
105
135
117
91
106
116
119
92
108
110
109
118
122
130
115
120
121
127
124
126
129
138
136
133
123
125
140
132
134
128
137
139
141
Hasse Diagram
(W Europe)
Iceland
Sweden
Finland
Norway
Austria
Greece
Switzerland
Spain
Portugal
France
Germany
Italy
Belgium
Netherlands
Ireland
Denmark
UK
Ranking Partially Ordered Sets – 2
An Example
Poset
(Hasse Diagram)
e
a
b
c
d
f
Jump Size: 3
Some linear extensions
a
a
a
b
b
c
c
b
a
a
b
e
c
c
c
e
b
d
d
e
d
d
e
e
d
f
f
f
f
f
1
5
4
2
Jump or Imputed Link (-------) is a link in the ranking that is not
implied by the partial order
Ranking Partially Ordered Sets – 3
In the example from the preceding slide, there are a total of 16 linear
extensions, giving the following frequency table.
Rank
Element
1
2
3
4
5
6
Totals
a
9
5
2
0
0
0
16
b
7
5
3
1
0
0
16
c
0
4
6
6
0
0
16
d
0
2
4
6
4
0
16
e
0
0
1
3
6
6
16
f
0
0
0
0
6
10
16
Totals
16
16
16
16
16
16
• Each (normalized) row gives the rank-frequency distribution for that element
• Each (normalized) column gives a rank-assignment distribution across the poset
Ranking Partially Ordered Sets – 5
Linear extension decision tree
Poset
(Hasse Diagram)
e
a
b
c
d
f
b
a
c
e
b
b
b
e
d
d
e
d
c
d
e
f
d
d
e
c
e
c
f
d
a
e
f
d e
d
f
e
d
a
c
c
f
e
f
e f f e f e f f e f e f e
Jump Size: 1 3 3 2 3 5 4 3 3 2 4 3 4 4 2 2
f
f
f
Ranking Partially Ordered Sets – 8
• In many cases of practical interest e(S) is too large for actual
enumeration in a reasonable length of time.
• For example, HEI data set has 141 countries arranged in a Hasse
diagram with 14 levels and level sizes
16, 14, 15, 12, 16, 24, 10, 9, 10, 7, 2, 2, 3, 1
This gives
8.6  10105  e(S)  1.9  10243
which is completely beyond present-day computational capabilities.
• So what do we do?
• Markov Chain Monte Carlo (MCMC) applied to the uniform
distribution on the set of all linear extensions lets us estimate the
normalized rank-frequency distributions. Estimating the absolute
frequencies (approximate counting) is also possible but somewhat more
difficult.
Cumulative Rank Frequency Operator – 5
An Example of the Procedure
In the example from the preceding slide, there are a total of 16 linear
extensions, giving the following cumulative frequency table.
Rank
Element
1
2
3
4
5
6
a
9
14
16
16
16
16
b
7
12
15
16
16
16
c
0
4
10
16
16
16
d
0
2
6
12
16
16
e
0
0
1
4
10
16
f
0
0
0
0
6
16
Each entry gives the number of linear extensions in which the element
(row) label receives a rank equal to or better that the column heading
Cumulative Rank Frequency Operator – 6
An Example of the Procedure
Cumulative Frequency
16
a
b
c
d
e
f
12
8
16
4
0
1
2
3
4
5
6
Rank
The curves are stacked one above the other and the result is a
linear ordering of the elements: a > b > c > d > e > f
Cumulative Rank Frequency Operator – 7
An Example where
Original Poset
F
(Hasse Diagram)
F
2
F
3
a
a
a
f
f
f
e
e
e
b
b
b
ad
ad
ad
c
c,g (tied)
h
h
f
a
b
c
F must be iterated
e
g
h
d
g
c
h
g
Breast Cancer by ZIP Code
New York State, 1993-1997
Simple SIRs as observed/expected
SIR (maximum likelihood estimate)
more than 100% above expected
50% to 100% above expected
15% to 49% above expected
within 15% of expected
15% to 50% below expected
more than 50% below expected
very sparse data
(28)
(93)
(279)
(471)
(338)
(100)
(104)
Ranking Possible Disease Clusters in
the State of New York
Data Matrix
cluster * SIR
LF2
LM14
LM4
LF7
B2
B4
LM1
LM3
LM7
2.09
1.5
2.04
1.51
1.21
1.25
2.32
2.13
2.12
LL
10.36
36
19.21
15.43
31.3
28.4
21.91
21.26
13.33
Young Multiple Atypical
Late Stage
Cases Cancers Demographics of Diagnosis
2
1
1
2
2
0
0
2
2
0
0
2
1
1
1
1
2
1
0
2
1
0
0
0
0
1
0
2
1
1
0
1
1
0
0
2
* LF = lung, female; LM = lung, male; B = breast
Multiple Criteria Analysis
Multiple Indicators and Choices
Health Statistics
Disease Etiology, Health Policy, Resource Allocation
• First stage screening
– Significant clusters by SaTScan and/or
upper surface level echelon sets
• Second stage screening
– Multicriteria noteworthy clusters
• Final stage screening
– Follow up clusters for etiology, intervention
based on multiple criteria
MARMAP SYSTEM
Software Design and Development
•
•
•
•
•
•
Algorithm development
Computer programming/Coding
User interface design and implementation
User output/Visualization
Documentation/On-line help
Other considerations:
– Supported platforms (Windows, UNIX ?, LINUX ?)
– Programming languages ? (C/C++, Java, Visual Basic,
Delphi, etc.)
– Software distribution (CD, Website)
CENTER FOR GEOSPATIAL
INFORMATICS AND
STATISTICS
-proposed federal partnershipNSF: DGP, FRG, ITR, SDSC-NPACI
NASA, USGS, EPA, USFS, NRCS, NASS,
DOT, NCHS, CDC, NCI, CENSUS, NIMA,
DOD, NOAA
Case Study –UNEP - PSU
• Nationwide Human Environment Index worldwide
• Construction and Evaluation of HEI
• Multiple Indicators and Comparisons without
Integration of Indicators
• Hasse Diagrams, fuzzy rankings, and
visualizations
• Handbook
• Interactive Queries
Case Study – NASA - PSU
• Issues Involved:
• Landcover classification
– with available spectral image(s)
– with a previous map and current spectral image
– with fine or coarse segmentation
• Multi-period change detection
• Data Integration
Case Study – EPA – PSU
Issues Involved:
• Indicators of Watershed Ecosystem Health
• Multiple Landscape Fragmentation Analysis
• Echelon Analysis of Spatial Structure and
Behavior
• Multiscale Bivariate Raster Map Analysis
• Regional Human Environment Index:
Formulation, Visualization, Evaluation, and
Validation
Partnership Synergistics
Concept
Software
Implementation
Prototype
Feedback
Case Studies
Pilot Tests
Partnership Synergistics
MG- PG
PI/CO-PI
CG
CSG
Partnership Synergistics
Methodology Group
Concepts, Issues, Approaches, Methods
Prototype Group
Techniques, Algorithms, Routines
Computational Group
Data Management,
Software Design and
Methodology Group
Development
Refinement, Adaptation, Development
MARMAP SYSTEM VALIDATION
MG, PG, CG, CSG
Case Studies
Data Resources
Issues
Answers
Logo for Statistics, Ecology,
Environment, and Society