README.md
In WeiquanLuo/stNet: What the Package Does (One Line, Title Case)
stNet
The goal of stNet is to remove the heterogeneity bewteen spatiotemporal
data by fusing the spatial and temporal feature with mapping to the
desired scale.
Installation
You can install the released version of stNet from
CRAN with:
devtools::install_github("WeiquanLuo/stNet")
# install.packages("stNet") # not yet avaliable
Workflow
Example
This is a basic example which shows a workflow of using (stNet)
functionality:
library(stNet)
# load data
data(corn_yield_sf); corn_yield_st <- corn_yield_sf # polygon sf object
data(weather_tmin_sf); weather_tmin_st <- weather_tmin_sf # point sf object
data(water_Temperature_sf); water_Temperature_st <- water_Temperature_sf # point sf object
# Visualization the data
plot(sf::st_geometry(corn_yield_st))
plot(sf::st_geometry(weather_tmin_st), col = "blue", add = TRUE)
plot(sf::st_geometry(water_Temperature_st), col = "red", add = TRUE)
Step 1 fuse the spatio heterogeneity: spatio_fuse()
yield_tmin_t <- spatio_fuse(target_stN = corn_yield_st,
data_stN = weather_tmin_st,
parm_nm = "tmin",
crs = 2163)
#> Linking to GEOS 3.7.2, GDAL 2.4.2, PROJ 5.2.0
yield_watertem_t <- spatio_fuse(target_stN = corn_yield_st,
data_stN = water_Temperature_st,
parm_nm = "watertem",
crs = 2163)
yield_watertem_t
#> # A tibble: 15 x 3
#> county target predictor
#> <chr> <list<df[,2]>> <list>
#> 1 bremer [39 × 2] <tibble [2,582 × 2]>
#> 2 clayton [39 × 2] <tibble [1,386 × 2]>
#> 3 clinton [39 × 2] <tibble [2,215 × 2]>
#> 4 dallas [39 × 2] <tibble [2,389 × 3]>
#> 5 dickinson [39 × 2] <tibble [2,648 × 3]>
#> 6 fremont [39 × 2] <tibble [1,035 × 2]>
#> 7 greene [39 × 2] <tibble [2,298 × 2]>
#> 8 hamilton [39 × 2] <tibble [2,339 × 2]>
#> 9 linn [39 × 2] <tibble [1,739 × 2]>
#> 10 marion [39 × 2] <tibble [2,544 × 2]>
#> 11 page [39 × 2] <tibble [2,269 × 2]>
#> 12 polk [39 × 2] <tibble [1,921 × 2]>
#> 13 pottawattamie [38 × 2] <tibble [3,264 × 2]>
#> 14 sac [39 × 2] <tibble [2,345 × 2]>
#> 15 woodbury [39 × 2] <tibble [2,611 × 2]>
yield_watertem_t$target[[1]]
#> # A tibble: 39 x 2
#> Year yield
#> <int> <dbl>
#> 1 2018 212.
#> 2 2017 213.
#> 3 2016 211.
#> 4 2015 203.
#> 5 2014 159
#> 6 2013 175.
#> 7 2012 133.
#> 8 2011 196.
#> 9 2010 175.
#> 10 2009 189.
#> # … with 29 more rows
yield_watertem_t$predictor[[1]]
#> # A tibble: 2,582 x 2
#> Date watertem_05458300
#> <date> <dbl>
#> 1 2011-10-01 13.5
#> 2 2011-10-02 12.9
#> 3 2011-10-03 13.7
#> 4 2011-10-04 14.6
#> 5 2011-10-05 15.5
#> 6 2011-10-06 16.1
#> 7 2011-10-07 16.5
#> 8 2011-10-08 17.2
#> 9 2011-10-09 17.5
#> 10 2011-10-10 16.8
#> # … with 2,572 more rows
Step 2: fuse temporal heterogeneity: tempo_fuse()
yield_tmin <- tempo_fuse(target_data = yield_tmin_t,
date_col = c("Year", "Date"),
scaling = c("Year","Month"),
aggMethod = c("mean","min"))
yield_watertem <- tempo_fuse(target_data = yield_watertem_t,
date_col = c("Year", "Date"),
scaling = c("Year","Month"),
aggMethod = c("mean","min"))
yield_watertem
#> # A tibble: 15 x 2
#> county data
#> <chr> <list>
#> 1 bremer <tibble [39 × 14]>
#> 2 clayton <tibble [39 × 14]>
#> 3 clinton <tibble [39 × 14]>
#> 4 dallas <tibble [39 × 26]>
#> 5 dickinson <tibble [39 × 26]>
#> 6 fremont <tibble [39 × 14]>
#> 7 greene <tibble [39 × 14]>
#> 8 hamilton <tibble [39 × 14]>
#> 9 linn <tibble [39 × 14]>
#> 10 marion <tibble [39 × 14]>
#> 11 page <tibble [39 × 14]>
#> 12 polk <tibble [39 × 14]>
#> 13 pottawattamie <tibble [38 × 14]>
#> 14 sac <tibble [39 × 14]>
#> 15 woodbury <tibble [39 × 14]>
yield_watertem$data[[1]]
#> # A tibble: 39 x 14
#> Year yield watertem_054583… watertem_054583… watertem_054583…
#> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 2018 107. 1 0.1 -0.1
#> 2 2017 107. 1 1 -0.1
#> 3 2016 106. 1 1 -0.2
#> 4 2015 102. 1 1 -0.1
#> 5 2014 80 1 -0.1 -0.1
#> 6 2013 88.0 1 0 -0.1
#> 7 2012 67.2 1 0.7 0.5
#> 8 2011 98.8 1 1 -0.1
#> 9 2010 87.8 NA NA NA
#> 10 2009 95.0 NA NA NA
#> # … with 29 more rows, and 9 more variables: watertem_05458300_1 <dbl>,
#> # watertem_05458300_2 <dbl>, watertem_05458300_3 <dbl>,
#> # watertem_05458300_4 <dbl>, watertem_05458300_5 <dbl>,
#> # watertem_05458300_6 <dbl>, watertem_05458300_7 <dbl>,
#> # watertem_05458300_8 <dbl>, watertem_05458300_9 <dbl>
Step 3: bind multiple homogeneous spatiotemporal data into a single object
If there multiple parameter for the same target varible, we can use
cbind_TargetDatas() to join them. For example, joining two objects
yield_tmin and yield_tmax by join_by = "Year" with anchoring the
target varialbe target = "yield":
bind_data <- cbind_TargetDatas(yield_tmin, yield_watertem,
target = "yield", join_by = "Year"); bind_data; bind_data$data[[1]]
#> # A tibble: 90 x 2
#> county data
#> <chr> <list>
#> 1 adair <tibble [39 × 14]>
#> 2 adams <tibble [39 × 14]>
#> 3 allamakee <tibble [39 × 14]>
#> 4 appanoose <tibble [38 × 26]>
#> 5 audubon <tibble [39 × 14]>
#> 6 benton <tibble [39 × 26]>
#> 7 blackhawk <tibble [39 × 14]>
#> 8 boone <tibble [39 × 26]>
#> 9 bremer <tibble [39 × 26]>
#> 10 buenavista <tibble [39 × 26]>
#> # … with 80 more rows
#> # A tibble: 39 x 14
#> Year yield tmin_USC0013343… tmin_USC0013343… tmin_USC0013343…
#> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 2018 75.2 -306 -211 -94
#> 2 2017 88.1 -211 -167 -133
#> 3 2016 95.6 -233 -178 -111
#> 4 2015 88.8 -228 -239 -200
#> 5 2014 85.2 -261 -256 -228
#> 6 2013 69.4 -189 -228 -117
#> 7 2012 52.7 -189 -200 -89
#> 8 2011 76.9 -206 -261 -133
#> 9 2010 70.0 -289 -267 -106
#> 10 2009 90.2 -289 -189 -161
#> # … with 29 more rows, and 9 more variables: tmin_USC00133438_4 <dbl>,
#> # tmin_USC00133438_5 <dbl>, tmin_USC00133438_6 <dbl>,
#> # tmin_USC00133438_7 <dbl>, tmin_USC00133438_8 <dbl>,
#> # tmin_USC00133438_9 <dbl>, tmin_USC00133438_10 <dbl>,
#> # tmin_USC00133438_11 <dbl>, tmin_USC00133438_12 <dbl>
Afterword: Fit Your Model!
After fusing the spatial and temporal heterogeneity of the data, you can
use other generic modeling technique to do analysis. For example:
# fitmodel = lm
fitmodel <- function(data){
model <- lm(yield ~ ., data=data %>% na.omit() %>% dplyr::select(-Year))
return(model)
}
F_model <- yield_tmin %>%
dplyr::mutate(model = data %>% purrr::map(fitmodel)) %>%
dplyr::mutate(statisics = model %>% purrr::map(.f = function(m) broom::glance(m))) %>%
tidyr::unnest(statisics); F_model
#> # A tibble: 89 x 14
#> county data model r.squared adj.r.squared sigma statistic p.value df
#> <chr> <lis> <lis> <dbl> <dbl> <dbl> <dbl> <dbl> <int>
#> 1 adair <tib… <lm> 0.353 0.152 13.9 1.76 0.121 10
#> 2 adams <tib… <lm> 0.559 0.406 11.4 3.66 0.00454 10
#> 3 allam… <tib… <lm> 0.528 0.304 13.1 2.36 0.0551 10
#> 4 appan… <tib… <lm> 0.645 0.309 15.7 1.92 0.0839 19
#> 5 audub… <tib… <lm> 0.227 -0.0209 16.9 0.916 0.526 10
#> 6 benton <tib… <lm> 0.488 0.0281 17.8 1.06 0.446 19
#> 7 black… <tib… <lm> 0.491 0.333 13.8 3.11 0.00972 10
#> 8 boone <tib… <lm> 0.638 0.312 12.4 1.96 0.0741 19
#> 9 bremer <tib… <lm> 0.503 0.309 14.1 2.59 0.0316 10
#> 10 buena… <tib… <lm> 0.714 0.396 11.9 2.25 0.0448 21
#> # … with 79 more rows, and 5 more variables: logLik <dbl>, AIC <dbl>,
#> # BIC <dbl>, deviance <dbl>, df.residual <int>
WeiquanLuo/stNet documentation built on Nov. 24, 2019, 5:11 p.m.
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Note that we can't provide technical support on individual packages. You should contact the package authors for that.
stNet
The goal of stNet is to remove the heterogeneity bewteen spatiotemporal data by fusing the spatial and temporal feature with mapping to the desired scale.
Installation
You can install the released version of stNet from CRAN with:
devtools::install_github("WeiquanLuo/stNet")
# install.packages("stNet") # not yet avaliable
Workflow
Example
This is a basic example which shows a workflow of using (stNet) functionality:
library(stNet)
# load data
data(corn_yield_sf); corn_yield_st <- corn_yield_sf # polygon sf object
data(weather_tmin_sf); weather_tmin_st <- weather_tmin_sf # point sf object
data(water_Temperature_sf); water_Temperature_st <- water_Temperature_sf # point sf object
# Visualization the data
plot(sf::st_geometry(corn_yield_st))
plot(sf::st_geometry(weather_tmin_st), col = "blue", add = TRUE)
plot(sf::st_geometry(water_Temperature_st), col = "red", add = TRUE)
Step 1 fuse the spatio heterogeneity: spatio_fuse()
yield_tmin_t <- spatio_fuse(target_stN = corn_yield_st,
data_stN = weather_tmin_st,
parm_nm = "tmin",
crs = 2163)
#> Linking to GEOS 3.7.2, GDAL 2.4.2, PROJ 5.2.0
yield_watertem_t <- spatio_fuse(target_stN = corn_yield_st,
data_stN = water_Temperature_st,
parm_nm = "watertem",
crs = 2163)
yield_watertem_t
#> # A tibble: 15 x 3
#> county target predictor
#> <chr> <list<df[,2]>> <list>
#> 1 bremer [39 × 2] <tibble [2,582 × 2]>
#> 2 clayton [39 × 2] <tibble [1,386 × 2]>
#> 3 clinton [39 × 2] <tibble [2,215 × 2]>
#> 4 dallas [39 × 2] <tibble [2,389 × 3]>
#> 5 dickinson [39 × 2] <tibble [2,648 × 3]>
#> 6 fremont [39 × 2] <tibble [1,035 × 2]>
#> 7 greene [39 × 2] <tibble [2,298 × 2]>
#> 8 hamilton [39 × 2] <tibble [2,339 × 2]>
#> 9 linn [39 × 2] <tibble [1,739 × 2]>
#> 10 marion [39 × 2] <tibble [2,544 × 2]>
#> 11 page [39 × 2] <tibble [2,269 × 2]>
#> 12 polk [39 × 2] <tibble [1,921 × 2]>
#> 13 pottawattamie [38 × 2] <tibble [3,264 × 2]>
#> 14 sac [39 × 2] <tibble [2,345 × 2]>
#> 15 woodbury [39 × 2] <tibble [2,611 × 2]>
yield_watertem_t$target[[1]]
#> # A tibble: 39 x 2
#> Year yield
#> <int> <dbl>
#> 1 2018 212.
#> 2 2017 213.
#> 3 2016 211.
#> 4 2015 203.
#> 5 2014 159
#> 6 2013 175.
#> 7 2012 133.
#> 8 2011 196.
#> 9 2010 175.
#> 10 2009 189.
#> # … with 29 more rows
yield_watertem_t$predictor[[1]]
#> # A tibble: 2,582 x 2
#> Date watertem_05458300
#> <date> <dbl>
#> 1 2011-10-01 13.5
#> 2 2011-10-02 12.9
#> 3 2011-10-03 13.7
#> 4 2011-10-04 14.6
#> 5 2011-10-05 15.5
#> 6 2011-10-06 16.1
#> 7 2011-10-07 16.5
#> 8 2011-10-08 17.2
#> 9 2011-10-09 17.5
#> 10 2011-10-10 16.8
#> # … with 2,572 more rows
Step 2: fuse temporal heterogeneity: tempo_fuse()
yield_tmin <- tempo_fuse(target_data = yield_tmin_t,
date_col = c("Year", "Date"),
scaling = c("Year","Month"),
aggMethod = c("mean","min"))
yield_watertem <- tempo_fuse(target_data = yield_watertem_t,
date_col = c("Year", "Date"),
scaling = c("Year","Month"),
aggMethod = c("mean","min"))
yield_watertem
#> # A tibble: 15 x 2
#> county data
#> <chr> <list>
#> 1 bremer <tibble [39 × 14]>
#> 2 clayton <tibble [39 × 14]>
#> 3 clinton <tibble [39 × 14]>
#> 4 dallas <tibble [39 × 26]>
#> 5 dickinson <tibble [39 × 26]>
#> 6 fremont <tibble [39 × 14]>
#> 7 greene <tibble [39 × 14]>
#> 8 hamilton <tibble [39 × 14]>
#> 9 linn <tibble [39 × 14]>
#> 10 marion <tibble [39 × 14]>
#> 11 page <tibble [39 × 14]>
#> 12 polk <tibble [39 × 14]>
#> 13 pottawattamie <tibble [38 × 14]>
#> 14 sac <tibble [39 × 14]>
#> 15 woodbury <tibble [39 × 14]>
yield_watertem$data[[1]]
#> # A tibble: 39 x 14
#> Year yield watertem_054583… watertem_054583… watertem_054583…
#> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 2018 107. 1 0.1 -0.1
#> 2 2017 107. 1 1 -0.1
#> 3 2016 106. 1 1 -0.2
#> 4 2015 102. 1 1 -0.1
#> 5 2014 80 1 -0.1 -0.1
#> 6 2013 88.0 1 0 -0.1
#> 7 2012 67.2 1 0.7 0.5
#> 8 2011 98.8 1 1 -0.1
#> 9 2010 87.8 NA NA NA
#> 10 2009 95.0 NA NA NA
#> # … with 29 more rows, and 9 more variables: watertem_05458300_1 <dbl>,
#> # watertem_05458300_2 <dbl>, watertem_05458300_3 <dbl>,
#> # watertem_05458300_4 <dbl>, watertem_05458300_5 <dbl>,
#> # watertem_05458300_6 <dbl>, watertem_05458300_7 <dbl>,
#> # watertem_05458300_8 <dbl>, watertem_05458300_9 <dbl>
Step 3: bind multiple homogeneous spatiotemporal data into a single object
If there multiple parameter for the same target varible, we can use
cbind_TargetDatas() to join them. For example, joining two objects
yield_tmin and yield_tmax by join_by = "Year" with anchoring the
target varialbe target = "yield":
bind_data <- cbind_TargetDatas(yield_tmin, yield_watertem,
target = "yield", join_by = "Year"); bind_data; bind_data$data[[1]]
#> # A tibble: 90 x 2
#> county data
#> <chr> <list>
#> 1 adair <tibble [39 × 14]>
#> 2 adams <tibble [39 × 14]>
#> 3 allamakee <tibble [39 × 14]>
#> 4 appanoose <tibble [38 × 26]>
#> 5 audubon <tibble [39 × 14]>
#> 6 benton <tibble [39 × 26]>
#> 7 blackhawk <tibble [39 × 14]>
#> 8 boone <tibble [39 × 26]>
#> 9 bremer <tibble [39 × 26]>
#> 10 buenavista <tibble [39 × 26]>
#> # … with 80 more rows
#> # A tibble: 39 x 14
#> Year yield tmin_USC0013343… tmin_USC0013343… tmin_USC0013343…
#> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 2018 75.2 -306 -211 -94
#> 2 2017 88.1 -211 -167 -133
#> 3 2016 95.6 -233 -178 -111
#> 4 2015 88.8 -228 -239 -200
#> 5 2014 85.2 -261 -256 -228
#> 6 2013 69.4 -189 -228 -117
#> 7 2012 52.7 -189 -200 -89
#> 8 2011 76.9 -206 -261 -133
#> 9 2010 70.0 -289 -267 -106
#> 10 2009 90.2 -289 -189 -161
#> # … with 29 more rows, and 9 more variables: tmin_USC00133438_4 <dbl>,
#> # tmin_USC00133438_5 <dbl>, tmin_USC00133438_6 <dbl>,
#> # tmin_USC00133438_7 <dbl>, tmin_USC00133438_8 <dbl>,
#> # tmin_USC00133438_9 <dbl>, tmin_USC00133438_10 <dbl>,
#> # tmin_USC00133438_11 <dbl>, tmin_USC00133438_12 <dbl>
Afterword: Fit Your Model!
After fusing the spatial and temporal heterogeneity of the data, you can use other generic modeling technique to do analysis. For example:
# fitmodel = lm
fitmodel <- function(data){
model <- lm(yield ~ ., data=data %>% na.omit() %>% dplyr::select(-Year))
return(model)
}
F_model <- yield_tmin %>%
dplyr::mutate(model = data %>% purrr::map(fitmodel)) %>%
dplyr::mutate(statisics = model %>% purrr::map(.f = function(m) broom::glance(m))) %>%
tidyr::unnest(statisics); F_model
#> # A tibble: 89 x 14
#> county data model r.squared adj.r.squared sigma statistic p.value df
#> <chr> <lis> <lis> <dbl> <dbl> <dbl> <dbl> <dbl> <int>
#> 1 adair <tib… <lm> 0.353 0.152 13.9 1.76 0.121 10
#> 2 adams <tib… <lm> 0.559 0.406 11.4 3.66 0.00454 10
#> 3 allam… <tib… <lm> 0.528 0.304 13.1 2.36 0.0551 10
#> 4 appan… <tib… <lm> 0.645 0.309 15.7 1.92 0.0839 19
#> 5 audub… <tib… <lm> 0.227 -0.0209 16.9 0.916 0.526 10
#> 6 benton <tib… <lm> 0.488 0.0281 17.8 1.06 0.446 19
#> 7 black… <tib… <lm> 0.491 0.333 13.8 3.11 0.00972 10
#> 8 boone <tib… <lm> 0.638 0.312 12.4 1.96 0.0741 19
#> 9 bremer <tib… <lm> 0.503 0.309 14.1 2.59 0.0316 10
#> 10 buena… <tib… <lm> 0.714 0.396 11.9 2.25 0.0448 21
#> # … with 79 more rows, and 5 more variables: logLik <dbl>, AIC <dbl>,
#> # BIC <dbl>, deviance <dbl>, df.residual <int>
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