In Spatial-R/EnvExpInd: Environmental Exposure on the Individual Level
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It is crucial to assess the indivudal exposure of environmental factors at different exposure periods (i.e, progestation and second trimester), so as to accurately assess how the air pollutant exposure work on the certain outcomes (i.e, birth defect and preterm labor). Compared with the traditional way which only uses the average concnetration among the whole monitoring sites, the EnvExpInd package taken the spatial heterogeneity into consideration and obtained a more accurate assessment of environmental exposure.
Dataset
Generally, three datasets including the concentration of environmental factors at each time point, individual information and the environmental monitoring sites should be prepared. Here, we take a simple example to illustrate how we can use the EnvExpInd packageto estimate the individual exposure of air pollutants.
- monitoring site: data.frame, contains the names of monitoring sites and their corresponding address.
- air pollution data: the concentration of environmental factors at each time point.
- individual information: detailed information of address and the start time to be estimated.
Procedure
- Obtain the longitude and latitude information for each individual as well as the monitoring sites according to their detailed address.
- Calculate the matched monitoring site (nearest monitoring site used as the matched site) or the corresponding spatial position after the interpolation.
- Based on the the time point of outcome and the assessment period, we can easily get the individual exposure in the targte period by re-running the second step.
Install
The package hasn't been published in R-Project，whereas, you can install it from the github (Spatial-R):
devtools::install_github("Spatial-R/EnvExpInd")
Load the dataset
After you have installed the EnvExpInd package, you can load the "envid" dataset in the package using the following codes: data("envind). The envid dataset contains the information about the air pollutants, montoring site and the detailed information for each patient. In order to omit the potential error in the following procedure, you should make sure that the variables regarding date information in the air pollutants and patient dataset should be formated as "date/time".
library(sp);
library(gstat);
library(EnvExpInd)
data("envind")
individual_data_tem$date <- as.Date(individual_data_tem$date);
pollutant_data$date <- as.Date(pollutant_data$date)
pollutant_data_tem <- pollutant_data
The individual_data_tem contains at least three variables: id (unique ID for the each patient), date (date of outcome occured, formated as "2014-10-20") and address (detailed address for each individual).
The pollutant_data also contains at least three variables: date (date of montoring, formated as "2014-10-20"), site_name (the name for the monitoring site) and concentration of certain air pollutants.
(Notes: There were some missing values in the pullutant dataset, therefore, you should make some interpolations to fill with)
The site_data contains at least two variables: site_name (the name for the monitoring site) and address (detailed address for the montoring site).
Analysis
Longitude and latitude information
There are several ways to get the longitude and latitude information based on the detailed address: Baidu, Google, and Amap. For the user in China, you can use the API of Baidu and Amap to transform the address into the longitude and latitude. Here, we provide a simple function get_latlon_china to help the uses to realise the process.
(Note: Daily call limitation for each api_key was 6000)
site_data <- get_latlon_china(data = site_data, add_var = "address", api_key = "your key")
individual_data_tem <- get_latlon_china(data = individual_data_tem, add_var = "address", api_key = "your key")
Exposure estimation
Generally, there were two ways to calculate the environmental exposure on the individual level: simple or complicated. The simple one here is that we choose the air pollutant concentration in the nearest monitoring site as the reference for each individual; however, for the complicated one, we use the spatial interpolation method (i.e, inverse distance weighted, Ordinary kriging, and land use regression) to assess the air pollutant concentration. In the current version of EnvExpInd, there are only two spatial interpolation methods available: inverse distance weighted and kriging.
- Simple one
Two improtant functions can be used to realised this aim: the function get_refrence_id_simple and expoure_estimate_simple. The function get_refrence_id_simple is used to match the nearest monitoring site for each individual. There were eight arugments (four arugments for the individual dataset and four arugments for the monitoring sites) that we should define firstly:
individual_data: dataframe with at least three variables: longitude, latitude and unique id. individual_lat: column name of latitude for each individual.individual_lon: column name of longitude for each individual.individual_id: column name of unique id for each individual. site_data: dataframe with at least three variables: longitude, latitude and unique id.site_lat: column name of latitude for each monitoring site.site_lon: column name of longitude for each monitoring site.site_id: column name of unique id for each monitoring site.
The codes are as followed:
individual_data_tem$refrence_id <- get_refrence_id_simple(individual_data = individual_data_tem,
individual_lat = "lat",
individual_lon ="lon",
individual_id = "id",
site_data = site_data,
site_lat = "lat",
site_lon = "lon",
site_id = "site")
kable(head(individual_data_tem[,-3]))
After that, we can use the function expoure_estimate_simple to get the exposure during the study period:
pollutant_data_tem$date <- as.Date(pollutant_data_tem$date)
individual_data_tem$date <- as.Date(individual_data_tem$date)
exposure.simple <- expoure_estimate_simple(individual_data = individual_data_tem,
individual_id = "id",
refrence_id = "refrence_id",
exposure_date = "date",
pollutant_data = pollutant_data_tem,
pollutant_site = "site.name",
pollutant_date = "date",
pollutant_name = c("PM10","SO2"),
estimate_interval = c(0:1))
kable(head(exposure.simple$PM10),digits= 1) #### PM10 estimation
id
day.0
day.1
2564
202
206
2563
206
220
2562
206
220
2561
230
200
2560
212
146
2559
176
136
kable(head(exposure.simple$SO2),digits= 1) #### SO2 estimation
id
day.0
day.1
2564
27
47
2563
47
60
2562
47
60
2561
82
52
2560
54
72
2559
44
52
- Inverse distance weighted
The spatial interpolation method can be realised using the gstat package. In the EnvExpInd package, we can use exposure_estimate_idw function to estimate the individual level exposure based on the inverse distance weighted method.
pollutant_data_tem_idw <- merge(pollutant_data_tem,site_data[,c(1,3,4)],by.x = "site.name",by.y = "site", all.x = T)
exposure.idw <- exposure_estimate_idw(individual_data = individual_data_tem,
individual_id = "id",
exposure_date ="date",
individual_lat ="lat",
individual_lon ="lon",
pollutant_data = pollutant_data_tem_idw,
pollutant_date = "date",
pollutant_site_lat = "lat",
pollutant_site_lon = "lon",
pollutant_name = c("PM10","SO2"),
estimate_interval = c(0:1))
kable(head(exposure.idw$PM10),digits = 1) #### PM10 estimation
id
day.0
day.1
2564
196.1
198.6
2563
220.2
198.6
2562
227.0
194.7
2561
191.0
224.1
2560
141.6
186.6
2559
136.1
176.1
kable(head(exposure.idw$SO2),digits= 1) #### SO2 estimation
id
day.0
day.1
2564
52.9
33.1
2563
72.1
53.0
2562
73.3
53.2
2561
50.8
81.0
2560
58.7
44.5
2559
51.9
44.0
- Ordinary kriging
Ordinary kriging to estimate the environmental exposure can be realised by the function exposure_estimate_krige. Generally, you should define the semivariogram firstly.
example.date <- range(pollutant_data_tem$date)[2]
test.pollutant <- filter(pollutant_data_tem,date == example.date)[,c(2,5)]
test.pollutant <- merge(test.pollutant,site_data,by.x = "site.name",by.y = "site")
coordinates(test.pollutant) <- ~lat + lon
m <- fit.variogram(variogram(PM10~1, test.pollutant), vgm(1, "Sph", 200, 1))
estimate.krige <- exposure_estimate_krige(individual_data = individual_data_tem,
individual_id = "id",
exposure_date = "date",
individual_lat = "lat",
individual_lon = "lon",
pollutant_data = pollutant_data_tem_idw,
pollutant_date = "date",
pollutant_site_lat = "lat",
pollutant_site_lon = "lon",
pollutant_name = c("PM10","SO2"),
estimate_interval = c(0:1),
krige_model = m,
nmax = 7,
krige_method = "med")
kable(head(estimate.krige$PM10),digits = 1) #### PM10 estimation
id
day.0
day.1
2564
194
198
2563
220
198
2562
220
194
2561
192
220
2560
136
178
2559
136
178
kable(head(estimate.krige$SO2),digits = 1) #### SO2 estimation
id
day.0
day.1
2564
58.0
35.0
2563
82.0
58.0
2562
80.0
58.0
2561
46.7
82.0
2560
52.0
46.7
2559
52.0
46.7
References
Li Wu, Lei Jin, Tingming Shi, Bing Zhang, et.al. Association between ambient particulate matter exposure and semen quality in Wuhan, China, Environment International, 98, 2017,219-228.
Tao Liu, Min Kang, Bing Zhang, et.al. Independent and interactive effects of ambient temperature and absolute humidity on the risks of avian influenza A(H7N9) infection in China. Science of The Total Environment, 2018, 619-620: 1358-1365.
BugReports
EnvExpInd is a package to estimate the environmental exposure on the individual level. If you have found any bugs, please set up an issue here.
Spatial-R/EnvExpInd documentation built on Oct. 24, 2020, 1:44 a.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
options(rmarkdown.html_vignette.check_title = FALSE) knitr::opts_chunk$set( collapse = TRUE, comment = "#>" )
It is crucial to assess the indivudal exposure of environmental factors at different exposure periods (i.e, progestation and second trimester), so as to accurately assess how the air pollutant exposure work on the certain outcomes (i.e, birth defect and preterm labor). Compared with the traditional way which only uses the average concnetration among the whole monitoring sites, the EnvExpInd package taken the spatial heterogeneity into consideration and obtained a more accurate assessment of environmental exposure.
Dataset
Generally, three datasets including the concentration of environmental factors at each time point, individual information and the environmental monitoring sites should be prepared. Here, we take a simple example to illustrate how we can use the EnvExpInd packageto estimate the individual exposure of air pollutants.
- monitoring site: data.frame, contains the names of monitoring sites and their corresponding address.
- air pollution data: the concentration of environmental factors at each time point.
- individual information: detailed information of address and the start time to be estimated.
Procedure
- Obtain the longitude and latitude information for each individual as well as the monitoring sites according to their detailed address.
- Calculate the matched monitoring site (nearest monitoring site used as the matched site) or the corresponding spatial position after the interpolation.
- Based on the the time point of outcome and the assessment period, we can easily get the individual exposure in the targte period by re-running the second step.
Install
The package hasn't been published in R-Project，whereas, you can install it from the github (Spatial-R):
devtools::install_github("Spatial-R/EnvExpInd")
Load the dataset
After you have installed the EnvExpInd package, you can load the "envid" dataset in the package using the following codes: data("envind). The envid dataset contains the information about the air pollutants, montoring site and the detailed information for each patient. In order to omit the potential error in the following procedure, you should make sure that the variables regarding date information in the air pollutants and patient dataset should be formated as "date/time".
library(sp); library(gstat); library(EnvExpInd) data("envind") individual_data_tem$date <- as.Date(individual_data_tem$date); pollutant_data$date <- as.Date(pollutant_data$date) pollutant_data_tem <- pollutant_data
The individual_data_tem contains at least three variables: id (unique ID for the each patient), date (date of outcome occured, formated as "2014-10-20") and address (detailed address for each individual).
The pollutant_data also contains at least three variables: date (date of montoring, formated as "2014-10-20"), site_name (the name for the monitoring site) and concentration of certain air pollutants. (Notes: There were some missing values in the pullutant dataset, therefore, you should make some interpolations to fill with)
The site_data contains at least two variables: site_name (the name for the monitoring site) and address (detailed address for the montoring site).
Analysis
Longitude and latitude information
There are several ways to get the longitude and latitude information based on the detailed address: Baidu, Google, and Amap. For the user in China, you can use the API of Baidu and Amap to transform the address into the longitude and latitude. Here, we provide a simple function get_latlon_china to help the uses to realise the process.
(Note: Daily call limitation for each api_key was 6000)
site_data <- get_latlon_china(data = site_data, add_var = "address", api_key = "your key") individual_data_tem <- get_latlon_china(data = individual_data_tem, add_var = "address", api_key = "your key")
Exposure estimation
Generally, there were two ways to calculate the environmental exposure on the individual level: simple or complicated. The simple one here is that we choose the air pollutant concentration in the nearest monitoring site as the reference for each individual; however, for the complicated one, we use the spatial interpolation method (i.e, inverse distance weighted, Ordinary kriging, and land use regression) to assess the air pollutant concentration. In the current version of EnvExpInd, there are only two spatial interpolation methods available: inverse distance weighted and kriging.
- Simple one
Two improtant functions can be used to realised this aim: the function get_refrence_id_simple and expoure_estimate_simple. The function get_refrence_id_simple is used to match the nearest monitoring site for each individual. There were eight arugments (four arugments for the individual dataset and four arugments for the monitoring sites) that we should define firstly:
individual_data: dataframe with at least three variables: longitude, latitude and unique id.individual_lat: column name of latitude for each individual.individual_lon: column name of longitude for each individual.individual_id: column name of unique id for each individual.site_data: dataframe with at least three variables: longitude, latitude and unique id.site_lat: column name of latitude for each monitoring site.site_lon: column name of longitude for each monitoring site.site_id: column name of unique id for each monitoring site.
The codes are as followed:
individual_data_tem$refrence_id <- get_refrence_id_simple(individual_data = individual_data_tem, individual_lat = "lat", individual_lon ="lon", individual_id = "id", site_data = site_data, site_lat = "lat", site_lon = "lon", site_id = "site") kable(head(individual_data_tem[,-3]))
After that, we can use the function expoure_estimate_simple to get the exposure during the study period:
pollutant_data_tem$date <- as.Date(pollutant_data_tem$date) individual_data_tem$date <- as.Date(individual_data_tem$date) exposure.simple <- expoure_estimate_simple(individual_data = individual_data_tem, individual_id = "id", refrence_id = "refrence_id", exposure_date = "date", pollutant_data = pollutant_data_tem, pollutant_site = "site.name", pollutant_date = "date", pollutant_name = c("PM10","SO2"), estimate_interval = c(0:1)) kable(head(exposure.simple$PM10),digits= 1) #### PM10 estimation
| id | day.0 | day.1 |
|---|---|---|
| 2564 | 202 | 206 |
| 2563 | 206 | 220 |
| 2562 | 206 | 220 |
| 2561 | 230 | 200 |
| 2560 | 212 | 146 |
| 2559 | 176 | 136 |
kable(head(exposure.simple$SO2),digits= 1) #### SO2 estimation
| id | day.0 | day.1 |
|---|---|---|
| 2564 | 27 | 47 |
| 2563 | 47 | 60 |
| 2562 | 47 | 60 |
| 2561 | 82 | 52 |
| 2560 | 54 | 72 |
| 2559 | 44 | 52 |
- Inverse distance weighted
The spatial interpolation method can be realised using the gstat package. In the EnvExpInd package, we can use exposure_estimate_idw function to estimate the individual level exposure based on the inverse distance weighted method.
pollutant_data_tem_idw <- merge(pollutant_data_tem,site_data[,c(1,3,4)],by.x = "site.name",by.y = "site", all.x = T) exposure.idw <- exposure_estimate_idw(individual_data = individual_data_tem, individual_id = "id", exposure_date ="date", individual_lat ="lat", individual_lon ="lon", pollutant_data = pollutant_data_tem_idw, pollutant_date = "date", pollutant_site_lat = "lat", pollutant_site_lon = "lon", pollutant_name = c("PM10","SO2"), estimate_interval = c(0:1)) kable(head(exposure.idw$PM10),digits = 1) #### PM10 estimation
| id | day.0 | day.1 |
|---|---|---|
| 2564 | 196.1 | 198.6 |
| 2563 | 220.2 | 198.6 |
| 2562 | 227.0 | 194.7 |
| 2561 | 191.0 | 224.1 |
| 2560 | 141.6 | 186.6 |
| 2559 | 136.1 | 176.1 |
kable(head(exposure.idw$SO2),digits= 1) #### SO2 estimation
| id | day.0 | day.1 |
|---|---|---|
| 2564 | 52.9 | 33.1 |
| 2563 | 72.1 | 53.0 |
| 2562 | 73.3 | 53.2 |
| 2561 | 50.8 | 81.0 |
| 2560 | 58.7 | 44.5 |
| 2559 | 51.9 | 44.0 |
- Ordinary kriging
Ordinary kriging to estimate the environmental exposure can be realised by the function exposure_estimate_krige. Generally, you should define the semivariogram firstly.
example.date <- range(pollutant_data_tem$date)[2] test.pollutant <- filter(pollutant_data_tem,date == example.date)[,c(2,5)] test.pollutant <- merge(test.pollutant,site_data,by.x = "site.name",by.y = "site") coordinates(test.pollutant) <- ~lat + lon m <- fit.variogram(variogram(PM10~1, test.pollutant), vgm(1, "Sph", 200, 1)) estimate.krige <- exposure_estimate_krige(individual_data = individual_data_tem, individual_id = "id", exposure_date = "date", individual_lat = "lat", individual_lon = "lon", pollutant_data = pollutant_data_tem_idw, pollutant_date = "date", pollutant_site_lat = "lat", pollutant_site_lon = "lon", pollutant_name = c("PM10","SO2"), estimate_interval = c(0:1), krige_model = m, nmax = 7, krige_method = "med") kable(head(estimate.krige$PM10),digits = 1) #### PM10 estimation
| id | day.0 | day.1 |
|---|---|---|
| 2564 | 194 | 198 |
| 2563 | 220 | 198 |
| 2562 | 220 | 194 |
| 2561 | 192 | 220 |
| 2560 | 136 | 178 |
| 2559 | 136 | 178 |
kable(head(estimate.krige$SO2),digits = 1) #### SO2 estimation
| id | day.0 | day.1 |
|---|---|---|
| 2564 | 58.0 | 35.0 |
| 2563 | 82.0 | 58.0 |
| 2562 | 80.0 | 58.0 |
| 2561 | 46.7 | 82.0 |
| 2560 | 52.0 | 46.7 |
| 2559 | 52.0 | 46.7 |
References
Li Wu, Lei Jin, Tingming Shi, Bing Zhang, et.al. Association between ambient particulate matter exposure and semen quality in Wuhan, China, Environment International, 98, 2017,219-228.
Tao Liu, Min Kang, Bing Zhang, et.al. Independent and interactive effects of ambient temperature and absolute humidity on the risks of avian influenza A(H7N9) infection in China. Science of The Total Environment, 2018, 619-620: 1358-1365.
BugReports
EnvExpInd is a package to estimate the environmental exposure on the individual level. If you have found any bugs, please set up an issue here.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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