Integrated Discrete Environmental Analytics System
Chiranjib Chaudhuri 2020-07-01
GitHub Documents
This document explains the analytic capabilities of the IDEAS data model.
##
## Attaching package: 'dplyr'
## The following objects are masked from 'package:stats':
##
## filter, lag
## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, union
## Linking to GEOS 3.8.0, GDAL 2.5.0, PROJ 6.3.0
We connect to a table containing spatial-time series of annual extreme daily climate variables for entire Canada.
data=tbl(con,"ANUSPLINE3")
head(data)
## # Source: lazy query [?? x 4]
## # Database: NetezzaConnection
## DGGID KEY VALUE TID
## <int> <chr> <dbl> <int>
## 1 2420704 MAX_TEMP 20.4 1950
## 2 2374744 MAX_TEMP 23.5 1950
## 3 2360784 MAX_TEMP 27.0 1950
## 4 2364464 MAX_TEMP 25.6 1950
## 5 2311463 MAX_TEMP 30.7 1950
## 6 2381424 MAX_TEMP 20.5 1950
Next we slice the data set for annual maximum daily precipitation.
datap=data%>%filter(KEY=='PRECIPITATION')
head(datap)
## # Source: lazy query [?? x 4]
## # Database: NetezzaConnection
## DGGID KEY VALUE TID
## <int> <chr> <dbl> <int>
## 1 2297589 PRECIPITATION 7.00 1950
## 2 2473831 PRECIPITATION 18.1 1950
## 3 2393550 PRECIPITATION 24.5 1950
## 4 2479111 PRECIPITATION 5.10 1950
## 5 2417910 PRECIPITATION 5.45 1950
## 6 2493712 PRECIPITATION 11.5 1950
We will calculate time-series of spatial average
avgs=datap%>%group_by(TID)%>%arrange(TID)%>%summarise(VALUE=mean(VALUE))
head(avgs)
## Warning: Missing values are always removed in SQL.
## Use `mean(x, na.rm = TRUE)` to silence this warning
## This warning is displayed only once per session.
## # Source: lazy query [?? x 2]
## # Database: NetezzaConnection
## TID VALUE
## <int> <dbl>
## 1 1950 17.7
## 2 1951 18.0
## 3 1952 19.6
## 4 1953 20.9
## 5 1954 21.1
## 6 1955 20.1
We will calculate spatial distribution of temporal average
avgt=datap%>%group_by(DGGID)%>%arrange(DGGID)%>%summarise(VALUE=mean(VALUE))
head(avgt)
## # Source: lazy query [?? x 2]
## # Database: NetezzaConnection
## DGGID VALUE
## <int> <dbl>
## 1 2460666 26.7
## 2 2277703 27.6
## 3 2414025 24.7
## 4 2481826 26.2
## 5 2266103 34.0
## 6 2292943 40.3
Let us plot some of these basic variables.
avgs=collect(avgs)
plot(avgs$TID,avgs$VALUE)
To plot the spatial
variable we need to attach it with the spatial tabls.
grid=tbl(con,"FINALGRID2")
head(grid)
## # Source: lazy query [?? x 6]
## # Database: NetezzaConnection
## DGGID RESOLUTION QUAD I J GEOM
## <dbl> <int> <int> <int> <int> <ODBC_bnr>
## 1 1.38e11 22 5 68729 95619 010100f0e599f2bc145bc06412aaabad145bc…
## 2 1.38e11 22 5 68733 95613 010100b495fa264b145bc0ec7e15e03b145bc…
## 3 1.38e11 22 5 68732 95610 0101009cc6accd35145bc02ce6e78626145bc…
## 4 1.38e11 22 5 68733 95597 01010068a8401c9a135bc0a434bcd58a135bc…
## 5 1.38e11 22 5 68726 95603 0101008cd03a702f145bc000a0ab2920145bc…
## 6 1.38e11 22 5 68728 95595 01010004a6fc39bf135bc0986898f3af135bc…
avgt=avgt%>%inner_join(grid,by=c('DGGID'))%>%mutate(WKT=inza..ST_AsText(GEOM))%>%
select(DGGID,VALUE,WKT)%>%arrange(DGGID)%>%head(100)%>%collect()
poly=st_as_sf(avgt, wkt='WKT', crs = 4326)
plot(poly['VALUE'])
Lets get a little more complex now. We want to clip the data for one of the eco-zone over Canada say somwhere over BC, Pacific-Maritime (ecozone=13)
ecozone=tbl(con,"ECOZONE_12")%>%filter(VALUE==13)%>%select(DGGID)
head(ecozone)
## # Source: lazy query [?? x 1]
## # Database: NetezzaConnection
## DGGID
## <int>
## 1 2290293
## 2 2290292
## 3 2212852
## 4 2213587
## 5 2214331
## 6 2216513
datape=datap%>%inner_join(ecozone,by=c('DGGID'))
head(datape)
## # Source: lazy query [?? x 4]
## # Database: NetezzaConnection
## DGGID KEY VALUE TID
## <int> <chr> <dbl> <int>
## 1 2222341 PRECIPITATION 89.3 1950
## 2 2222341 PRECIPITATION 89.3 1950
## 3 2231102 PRECIPITATION 34.2 1950
## 4 2231102 PRECIPITATION 34.2 1950
## 5 2233302 PRECIPITATION 25.0 1950
## 6 2233302 PRECIPITATION 25.0 1950
Last update: July 1, 2020