1. Application of ArcGIS Geostatistical
Analyst for Interpolating Precipitation
and Temperature Data from Monitoring
Stations
Pedcris M. Orencio
MC1, 76093201
Graduate School of Environmental Science
Hokkaido University

2. Introduction
• Monitoring stations such as weather stations
collect information on precipitation and
temperature to determine climatological
changes
• Geographical Information System (GIS)
provides the means for the management,
visualization and analysis of the monitored data
• ArCGIS geostatistical analyst tool can be used
for spatial exploration and surface information
generation from discrete points using statistical
methods

3. What is Interpolation?
• interpolation is a method of constructing new data
points within the range of a discrete sets of known
data points.
• it can either be deterministic or probabilistic.
• Deterministic methods create a continuous surface
by only using the geometric characteristics of point
observations or mathematical computations.
(example: IDW)
• Probabilistic methods are based on probabilistic
theory and use the concept of randomness, and
include the variance in the process of computing
the statistical significance of the predicted values to
establish cetainty. (example: Kriging)

4. Objectives and Purpose
• Understand the capability of some tools in
ArcGIS Geostatistical Analyst in interpolating
monitoring data into spatial form
• Define the trends or the shift in seasonal
precipitation and temperature changes (during
Winter, Spring, Summer and Autumn) in
Hokkaido
• Understand the concentration areas of
temperature and precipitation and identify
spatially, the local maxima and minima for each
seasons

5. Methodology
• Gather secondary data from JMA
• 154 stations in Hokkaido (AMEDAS and Weather
Stations)
• 1971 to 2000 average precipitation and temperature
• Data processing and assumption setting
• Data manipulation using ArcGIS Geostatistical Analyst
• Elevation using Inverse Distance Weighting
• Average Precipitation and Temperature per Season
using Cross Validation and Kriging Methods
• Data Analysis
• Overlay of Elevation Maps, Average Temperature and
Precipitation Maps

6. Process Flow
Secondary data gathered from JMA
(http://www.data.jma.go.jp)
Data processing using MS Excel
• Station
• Bearing (Latitude & Longitude)
• Elevation
•Precipitation and Temperature Averages
(Monthly Average of 1971- 2000 data)
Data manipulation
using ArcGIS Map form for overlay and analysis

7. Preparing the Thematic Maps
Elevation and Average Average
Stations
Bearing Precipitation per Temperature per
Month Month
(1971- 2000) (1971- 2000)
Elevation and W S S A W S S A
Stations
Bearing i p u u i p u u
n r m t n r m t
t i m u t i m u
e n e m e n e m
r g r n r g r n
Interpolation
Data Base Map Thematic Map
Method

8. Hokkaido Sampled Weather Stations

9. Hokkaido Map of Weather Stations
Sapporo
Base Map: ArcGIS 9 ESRI Data & Maps 9.3
Coordinate System: GCS_JGD_2000

10. Elevation using Interpolation (IDW)
5 Color Class limits
Classes (Elevation)
Highest 432- 540
Nukabira
High 324- 432
Mid 216- 324
(540 m)
Low 108- 216
Lowest 0- 108
in meters
Inverse Distance Weighting (IDW) is a quick deterministic interpolator that
is exact. There are no assumptions required of the data- no assessment of
prediction errors, thus IDW can produce "bulls eyes" around data locations

11. Estimated Ave. Precipitation by Season
Precipitation Ave. in mm.
Seasons (1971 to 2000)
Highest Class Limit Lowest Class Limit
Winter
(November to March)
175.92- 196.5 31.90- 52.47
Spring
(April to June)
154.84- 170.50 45.27- 60.92
Summer
(July to August)
290.25- 320.80 76.40- 106.95
Autumn
(Sept to October)
275.85- 303.25 84.05- 111.45

12. Trends in Ave. Precipitation by Season (Kriging)
High
Autumn Winter
Low
Spring Summer

13. Estimated Ave. Temperature by Season
Temperature Ave. in °C.
Seasons (1971 to 2000)
Highest Class Limit Lowest Class Limit
Winter
(November to March)
0.99- 2.10 -6.80- -5.69
Spring
(April to June) 11.15- 11.87 6.13- 6.85
Summer
(July to August) 20.18- 21.30 12.35- 13.47
Autumn
(Sept to October) 15.70- 16.60 9.40- 10.30

14. Trends in Ave. Temperature by Season (Kriging)
High
Autumn Winter
Low
Spring Summer

15. Analysis Using Map Overlays
• Seasonal Ave. Precipitation by Elevation (example:
Average Precipitation in Autumn)
Darker shade means higher
average precipitation while
lighter shade means lower
average precipitation

16. Analysis Using Map Overlays
• Seasonal Ave. Temperature by Elevation (example:
Average Temperature in Winter)
Decrease in shade of color of
elevated areas means that there is
low average temperature in these
areas while increase in shade
means higher temperature

17. Analysis Using Map Overlays
• Precipitation-Temperature Relationships (example: Spring
Season)
Darker shade of colors means
that there are higher average
temperature in higher average
precipitation

18. Seasonal Ave. Precipitation by Elevation
Autumn Winter
Spring Summer

19. Seasonal Ave. Temperature by Elevation
Autumn Winter
Spring Summer

20. Precipitation-Temperature Relationship
Autumn Winter
Spring Summer

21. Summary
• Climatic data map have varying degrees of utilization
• Land and environmental conservation, production, management
• ArcGIS Geospatial Analyst can be used to produce spatial
representation of monitored data using interpolation
methods
• Desirable in establishing trend (Kriging method)
• Calculating spatial pattern in identifying mean or median in large
areas (IDW method)
• The quality of produced maps are influenced by a number of
factors as this determines the capability of the technique
estimation in interpolating the surface
• Reliability of the data sets gathered from the points and the other
factors that affects the samples measured
• Frequency and distance of the points from one another