1. Spatial Modeling of Mosquito Densities Using MODIS Enhanced Vegetation Index (EVI) and Near Ground Humidity Indexes: adult female Culex tarsalis and Aedes vexans clustering in Colorado and Louisiana
A PRESENTATION TO THE SUMMER COLLOQUIUM ON CLIMATE AND HEALTH
JULY 23, 2004, NCAR, BOULDER COLORADO
RUSSELL BARBOUR PH.D.
VECTOR ECOLOGY LABORATORY
YALE SCHOOL OF MEDICINE
NEW HAVEN CT.

2. MODELING VERSUS INTERPOLATION
LINEAR MODELING ATTEMPTS TO IDENTIFY FACTORS THAT INFLUENCE THE PARAMETERS OF INTEREST AND EXPLAIN OBSERVED VARIATION
SPATIAL MODELING OR INTERPOLATION USE THE MATHEMATICAL PROPERTIES OF THE DATA ITSELF TO ESTIMATE VALUES AT UNKNOWN LOCATIONS

3. LECTURE OUTLINE Review basic concepts of spatial auto-correlation
Demonstrate application of these methods to estimate mosquito vectors of West Nile Virus

4. BASIC CONCEPTS OF SPATIAL AUTO-CORRELATION
Tobler’s first law of geography:
Everything is related to everything else, but near things are more related than distant things
Auto- Correlation violates the assumption of independence in that is made in most statistical tests
Ordinary Least Squares Regression (OLS) for example, will tend to Type I Error ( falsely find significant relationships) if auto-correlation is present
Auto-Correlation can be used to estimate values at un-sampled locations

5. QUANTIFYING AUTO-CORRELATION
Moran’s I
Geary’s C ratio
Anselin’s Local Index of Spatial Autocorrelation (LISA) 0R Local Moran’s I

6. Moran’s I
Similar to Pearson’s correlation coefficient, values between –1.0 and + 1.
Index for dispersion/random/cluster patterns
Indices close to zero, indicate random pattern
Indices above zero indicate a tendency toward clustering
Indices below zero indicate a tendency toward dispersion/uniform
Most commonly reported indicator of spatial auto-correlation
Differences from correlation coefficient are:
one variable only, not two variables
Incorporates weights (wij) which index “distance” between the locations

7. MORAN’S I CONTINUED GLOBAL MORANS’ I
Estimates the level of aggregation of values or clustering in space for all observations
Correlogram
Morans’I calculated for observations grouped into specific distances

8. TYPES OF SPATIAL STRUCTURE DETECTED BY POSITIVE MORANS’I VALUES
CLUSTERS:
DATA FOUND IN CLOSE PROXIMITY
TRENDS:
A GRADIENT USUALLY CAUSED BY A GEOGRAPHIC FEATURE (NON- STATIONARITY)
AUTO-CORRELATION:
SIMILARITY OF OBSERVATIONS CLOSE TO EACH OTHER. A CLUSTER MAY OR MAY NOT HAVE AUTO- CORRELATION

9. STATIONARITY IN SPACE FIRST ORDER (STRICT) STATIONARITY
A property of a spatial process where all of the spatial random variables have the same mean and variance value.
INTRINSIC (WEAK) STATIONARITY
An assumption that the data comes from a random process with a constant mean, and a semivariogram that only depends on the distance and direction separating any two locations.
SOURCE : U. OF ARIZONA

10. PURPOSE OF LOUISIANA SPATIAL MOSQUITO ESTIMATES
INDICATE AREAS OF HUMAN RISK OF WEST NILE VIRUS
ASSIST DECISION MAKERS FOR VECTOR CONTROL INTERVENTIONS
ASSESS THE EFFECTIVENESS OF CONTROL MEASURES
ESTIMATES HAVE NO EXPLANATORY VALUE, STRICTLY A PROCESS OF CAPTURING MATHEMATICAL RELATIONSHIPS

11. Aedes vexans FLOOD WATER MOSQUITO STRONG FLIER > 24 Km/ DAY
SOURCE : SERVICE 1976
FLOOD WATER MOSQUITO
STRONG FLIER > 24 Km/ DAY
AGGRESSIVE HUMAN BITER
LOW INFECTION RATES
HIGH TRANSMISSION EFFICIENCY IF SYSTEMICALLY INFECTED (TURELL 2001)

12. Aedes vexans NJ Light Trap Catches 2003 St Tammany
MONTH
NUMBER
% OF TOTAL
JUNE
30312
65.01%
MAY
8144
24.69%
MARCH
1565
APRIL
6243
21.52%

14. NJ LIGHT TRAP CATCHES JUNE 2003
GLOBAL MORANS’ I
Aedes vexans
NJ LIGHT TRAP CATCHES JUNE 2003
ST. TAMMANY PARISH LA
Spatial Autocorrelation for Point Data:
Sample size
  Moran's "I"
Spatially random (expected) "I"
Standard deviation of "I"
Normality significance (Z) = P < .05
Randomization significance (Z) = P < .05

15. GLOBAL MORANS I VALUES Aedes vexans Catches NJ Light Traps 2003
MONTH
OBS I
EXP I P
FEB
-0.02
-.019
0.9656
MARCH
0.037
-0.019
0.1402
APRIL
0.081
0.0087
MAY
0.167
.0000
JUNE
0.090
0.0031

16. CORRELOGRAM FOR JUNE NJ LIGHT TRAP CATCHES
ALL SPECIES
APRIL 2003
ST TAMMANY PARISH LA.
RANGE
meters

17. ISOTROPIC VARIOGRAM Aedes vexans APRIL 2003
SAMPLE VARIANCE

18. MODIS ATMOSPHERE PRODUCTS
1-KM SPATIAL RESOLUTION
USING THE NEAR-INFRARED ALGORITHM DURING THE DAY, 1-KM PIXEL RESOLUTION
THE SOLAR RETRIEVAL ALGORITHM RELIES ON OBSERVATIONS OF WATER-VAPOR ATTENUATION OF REFLECTED SOLAR RADIATION IN THE NEAR-INFRARED IN THE ATMOSPHERE CLOSE TO THE GROUND
VALUES REPRESENT THE AMOUNT OF WATER PER PIXEL THAT COULD THEORETICALLY BE PRECIPITATED OUT OF THE ATMOSPHERE

19. MODIS ATMOSPHERE PRODUCT RELATIONSHIP TO Aedes vexans
WATER COLUMN PRODUCTS BY NIR AND IR ARE APPROXIMATIONS OF ABSOLUTE HUMIDITY AND SATURATION DEFICIT IN THE LOWER ATMOSPHERE
THE IR DATA IS PRODUCED FOR BOTH DAY AND NIGHT.. INCLUDES DUSK AND DAWN ESTIMATES
IS AVAILABLE ON A DAILY BASES

20. MEASUREMENT OF Aedes vexans MICRO-CLIMATE DISPERSAL PARAMETERS
MODIS WATER VAPOR
PRODUCTS
RAINFALL
STANDING WATER
HIGH ABSOLUTE HUMIDITY
HATCHING >EMERGENCE > DISPERSAL
LIGHT TRAP DATA
AND VARIOGRAPHY
CLUSTERING NEAR HOSTS
MORANS’I

23. RAINFALL ON Ae. vexans CLUSTERING CLUSTERING ON MODIS VALUES
P VALUE .54
CLUSTERING ON MODIS VALUES
P VALUE .104

24. SPATIAL FACTORS ASSOCIATED WITH Aedes vexans DENSITY IN St TAMMANY PARISH
MODIS WATER VAPOR PRODUCT .38
URBAN PRESENCE (POPULATION AND LIGHT) .34

25. MODIS INFRARED WATER VAPOR COLUMN
MONTHLY DATA 2003
TEMPERATURES ABOVE 90 F

26. Aedes vexans CATCHES in NJ LIGHT TRAPS
VERSUS MODIS HUMIDITY MEASURES 2003
START OF HIGH
TEMPERATURE

27. LAKE PONCHATRAIN

28. ESTIMATED DENSITY OF Aedes vexans ADULTS
BY CO-KRIGING LIGHT TRAP AND MODIS HUMIDITY DATA

29. LIGHT TRAP DATA WITH WEAKER SPATIAL STRUCTURE
Culex tarsalis IS INCRIMINATED IN THE TRANSMISSION OF WEST NILE VIRUS TO HUMANS IN THE FORT COLLINS COLORADO AREA (NASCI ET AL 2003)
BREEDS IN ANY SOURCE OF FRESH WATER OTHER THAN TREE HOLES . MULTIPLE GENERATIONS
IRRIGATION DITCHES HIGHLY FAVORABLE BREEDING AREAS
FEEDS ON BIRDS THEN SHIFTS TO MAMMALS AND HUMANS AS ABUNDANCE INCREASES

30. Culex tarsalis

31. Culex tarsalis DISPERSAL From (Reisen 2002)
SLOW MOVING, 1 Km/ DAY
WIND DRIVEN
ACTIVITY RELATED TO VEGETATION COVER

32. Culex tarsalis Clustering near Ft Collins Col. July 2003
Spatial Autocorrelation for Point Data:
Sample size
Moran's "I"
Spatially random (expected) “I”
Standard deviation of "I“
Normality significance (Z) =p> .10
Randomization significance (Z) =p> .10

33. PRESENCE OF IRRIGATED FARM LAND .26 EVI .12 WATER VAPOR DATA .34
SPATIAL RELATIONSHIPS OF C. tarsalis LIGHT TRAP DATA AND REMOTELY SENSED MICROCLIMATE INDICATORS
MODIS REMOTELY SENSED VEGETATION INDEX = ENHANCED VEGETATION INDEX (EVI)
MODIS WATER VAPOR COLUMN IR DATA
INDICATORS
SPATIAL RELATIONSHIP
PRESENCE OF IRRIGATED FARM LAND
.26
EVI
.12
WATER VAPOR DATA
.34

34. APPLICATION OF ARTIFICIAL NEURAL NETWORKING (ANN) TO IMPROVE ESTIMATES
IRRIGATION, VEGETATION AND WATER VAPOR INDICATORS COMBINED
BY ARTIFICIAL NEURAL NETWORKING (ANN)
RESPONSE SURFACE
ANN RESPONSE SURFACE = .61 SPATIAL RELATIONSHIP WITH
C. tarsalis LIGHT TRAP CATCHES

35. MODIS ENHANCED VEGETATION INDEX (EVI)
2003
MAY 9
JUNE 10
JULY 2

36. CLUSTERING OF C. tarsalis in RELATIONSHIP TO MODIS EVI VALUES
SPATIAL ASSOCIATION WITH
EVI VALUES OF
DURING MOST OF THE 2003
SEASON

37. RESULTS
AN ASSOCIATION APPEARS TO EXIST BETWEEN C. tarsalis AND MODIS EVI AND WATER VAPOR VALUES AT THE COLORADO SITE
A STRONGER ASSOCIATION BETWEEN Aedes vexans AND MODIS HUMIDITY DATA WAS FOUND AT THE LOUISIANA SITE
EVEN WEAKLY CLUSTERING SPECIES CAN BE ESTIMATED THROUGH APPLICATION OF SPATIAL STATISTICS AND ARTIFICIAL NEURAL NETWORKS
MORE ROBUST INTERPRETATION OF LIGHT TRAP DATA IS POSSIBLE
DAILY MODIS ATMOSPHERIC DATA AVAILABILITY WILL ALLOW FORWARD LOOKING MODELS IN THE NEAR FUTURE