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 HEALTHJULY 23, 2004, NCAR, BOULDER COLORADO
RUSSELL BARBOUR PH.D.
VECTOR ECOLOGY LABORATORY
YALE SCHOOL OF MEDICINE
NEW HAVEN CT.
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
LECTURE OUTLINE
• Review basic concepts of spatial auto-correlation
• Demonstrate application of these methods to estimate mosquito vectors of West Nile Virus
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
QUANTIFYING AUTO-CORRELATION
Moran’s I
Geary’s C ratio
Anselin’s Local Index of Spatial Autocorrelation (LISA) 0R Local Moran’s I
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
MORAN’S I CONTINUED
• GLOBAL MORANS’ IEstimates the level of aggregation of values or clustering in space for
all observations
• Correlogram Morans’I calculated for observations grouped into specific distances
TYPES OF SPATIAL STRUCTUREDETECTED 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
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
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
Aedes vexans
• FLOOD WATER MOSQUITO
• STRONG FLIER > 24 Km/ DAY
• AGGRESSIVE HUMAN BITER
• LOW INFECTION RATES
• HIGH TRANSMISSION EFFICIENCY IF SYSTEMICALLY INFECTED (TURELL 2001)
SOURCE : SERVICE 1976
Aedes vexans NJ Light Trap Catches 2003 St Tammany
MONTH NUMBER % OF TOTAL
JUNE 30312 65.01%
MAY 8144 24.69%
MARCH 1565 24.69%
APRIL 6243 21.52%
Spatial Autocorrelation for Point Data:--------------------------------------- Sample size 53 Moran's "I" 0.090325 Spatially random (expected) "I" -0.019231 Standard deviation of "I" 0.040462 Normality significance (Z) 2.707580 = P < .05 Randomization significance (Z) 2.952694 = P < .05
GLOBAL MORANS’ I Aedes vexans
NJ LIGHT TRAP CATCHES JUNE 2003 ST. TAMMANY PARISH LA
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 -0.019 0.0087
MAY 0.167 -0.019 .0000
JUNE 0.090 -0.019 0.0031
CORRELOGRAM FOR JUNE NJ LIGHT TRAP CATCHES ALL SPECIES APRIL 2003
ST TAMMANY PARISH LA.
meters
RANGE
ISOTROPIC VARIOGRAM Aedes vexans APRIL 2003
SAMPLE VARIANCE
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
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
MEASUREMENT OF Aedes vexans MICRO-CLIMATE DISPERSAL PARAMETERS
HATCHING >EMERGENCE > DISPERSAL
RAINFALL STANDING WATER HIGH ABSOLUTE HUMIDITY
CLUSTERING NEAR HOSTSLIGHT TRAP DATAAND VARIOGRAPHY
MORANS’I
MODIS WATER VAPOR PRODUCTS
Aedes vexans Monthly Catches NJ Light Traps VersusMODIS WATER COLUMN IR METHOD VALUES
(DAY + NIGHT ) FEB- JUNE 2003
-5000
0
5000
10000
15000
20000
25000
30000
35000
1 1.5 2 2.5 3 3.5
MODIS WATER VAPOR VALUES
MO
SQ
UIT
O N
UM
BE
RS Y
Predicted Y
Series3
Linear (Predicted Y)
MONTHLY Aedes vexans NJ LIGHT TRAP CATCH NUMBERS VERSUS RAINFALL
-10000
-5000
0
5000
10000
15000
20000
25000
30000
35000
0 2 4 6 8 10 12 14 16 18 20
RAINFALL IN INCHES
Ae
de
s v
ex
an
s n
um
be
rs
Y
Predicted Y
Linear (Predicted Y)
0
1
2
3
0 2 4 6 8
Y
Y
Predicted Y
RAINFALL ON Ae. vexans CLUSTERINGP VALUE .54
-0.4-0.2
00.20.40.60.8
1
0 1 2 3
Y
Y
Predicted Y
CLUSTERING ON MODIS VALUESP VALUE .104
SPATIAL FACTORS ASSOCIATED WITH Aedes vexans DENSITY IN
St TAMMANY PARISH
• MODIS WATER VAPOR PRODUCT .38
• URBAN PRESENCE (POPULATION AND LIGHT) .34
TEMPERATURES ABOVE 90 F
MODIS INFRARED WATER VAPOR COLUMN MONTHLY DATA 2003
MONTH MOSQUITO # MODIS Monthly Product
1 22 1.41
2 5 1.48
3 1565 1.52
4 6243 1.79
5 8144 2.57
6 30312 3.39
7 1322 3.93
8 494 4.01
9 315 3.14
10 1662 2.64
11 1084 2.37
12 97 1.64
Aedes vexans CATCHES in NJ LIGHT TRAPS VERSUS MODIS HUMIDITY MEASURES 2003
START OF HIGH TEMPERATURE
LAKE PONCHATRAIN
ESTIMATED DENSITY OF Aedes vexans ADULTS BY CO-KRIGING LIGHT TRAP AND MODIS HUMIDITY DATA
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
Culex tarsalis
Culex tarsalis DISPERSALFrom (Reisen 2002)
• SLOW MOVING, 1 Km/ DAY
• WIND DRIVEN
• ACTIVITY RELATED TO VEGETATION COVER
Culex tarsalis Clustering near Ft Collins Col. July 2003
Spatial Autocorrelation for Point Data:--------------------------------------- Sample size 70 Moran's "I" -0.010883 Spatially random (expected) “I” -0.014493 Standard deviation of "I“ 0.020686 Normality significance (Z) 0.174495 =p> .10 Randomization significance (Z) 0.179128 =p> .10
• MODIS REMOTELY SENSED VEGETATION INDEX = ENHANCED VEGETATION INDEX (EVI)
• MODIS WATER VAPOR COLUMN IR DATA
SPATIAL RELATIONSHIPS OF C. tarsalis LIGHT TRAP DATA AND REMOTELY
SENSED MICROCLIMATE INDICATORS
INDICATORS SPATIAL RELATIONSHIP
PRESENCE OF IRRIGATED FARM LAND
.26
EVI .12
WATER VAPOR DATA .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
MODIS ENHANCED VEGETATION INDEX (EVI)2003
JUNE 10
MAY 9
JULY 2
CLUSTERING OF C. tarsalis in RELATIONSHIP TO MODIS EVI VALUES
SPATIAL ASSOCIATION WITH EVI VALUES OF 4000- 6000 DURING MOST OF THE 2003 SEASON
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