Household table¶

Note: This document captures information that may have been updated. Please refer to the Summary and findings section instead.

Summary¶

Pre/Post variables: A number of variables in the Household table seek to capture information about the difference in living conditions of the households, before and after the earthquake. These include:

Residence (respreq, resposq)

Source of Water (h2o_pre, h2o_pos)

Source of cooking fuel (fir_pre, fir_pos)

Source of light (lit_pre, lit_pos)

Type of toilet (toilet_pre, toilet_pos)

Type of fixed assets owned (ast_pre, ast_pos), this variable is also a multiselect variable (refer to 3.)

One thing that we will need to pay attention to is regarding the way in which we aggregate this information at a building level, given that there may be more than one HHD per building.

Variables with no definitions in the data dictionary: We weren’t able to locate information for certain variables in the data dictionary.

Numeric: Some variables look like they may be of type Numeric. They include:

age: It is not so clear as to what this variable represents (age of the respondent vs. age of the building?)

hhd_size: It may contain information around the size of the household, but not sure

death_cn: Count of people who died post earthquake.

loss_cn: Count of people who were badly injured/missing post earthquake.

edrop_cn: Count of people who left their education post earthquake.

pdrop_cn: Count of pregnant women who stopped checkups earthquake.

vdrop_cn: Count of children who stopped getting vaccinated post earthquake.

oc_ch_cn: Count of people who had to change their occupation post earthquake.

Further investigation is needed as to what these columns mean.

There were two additional variables without any definitions. These columns only contained NAs for the subset of data that we analyzed. respreqd and resposqd

Multiselect Variables: The pre/post variables ast_pre and ast_pos are multiselect variables and require treatment

Special Case - Rahat: This column supposedly captures “the type of earthquake victim id card recieved” This also looks like a multiselect variable, but will need to confirm with the app developers as to why it was made a multiselect variable? And how values inside it are supposed to be interpreted.

Methodology¶

This section contains code used for the analysis, please jump to the next section for results

A small subset of 5000 rows were taken as an input for analysis. All variables in the table were converted to factors, and a summary was yielded from each table.

# Exploration of household table from pgSql database
library(RPostgreSQL)
library(dplyr)

root.dir <- "~/hrrp/openhrrp-src"

pg <- dbDriver("PostgreSQL")
con <- dbConnect(pg, user="postgres", password="postgres",
                 host="localhost", dbname="openhrrp")

##
# dbExistsTable(con, "household")

df_household <- dbGetQuery(con, "SELECT * from household limit 5000; ")
df_household <- as.data.frame(sapply(df_household, as.numeric))

write.csv(as.data.frame(colnames(df_household)), file=paste0(root.dir, "/outputs/householdColNames.csv"))

df_household_af <- as.data.frame(sapply(df_household, as.factor))


summarizeColumn <- function(column) {
  return (summary(column))
}

summarizeColumn(df_household_af$vdrop_cn)

Results¶

The table below outlines current status of the same:

Column Name	Present in building data dictionary	Name	Type	Is pre/post	Range	Is multiselect?	Comments
dist
vcode
vdcmun
ward
EA
howner_sn
house_sn
hhd_sn
gender	Yes	Gender of household head	Categorical		1-3
age	No		Numerical				Dont know what this means exactly, can it be the age of the person responding/ or the building?
Ishhd	Yes	Is rspondent household head	Categorical		1-2
rel_hres	Yes	Respondent’s relation to household head	Categorical		1-10		Data will have NA’s because it is only filled for Ishhd=2
id_type	Yes	Household head’s ID card type	Categorical		1-6
cast	Yes	Cast/Ethnicity of Household Head	Categorical		1-140, 990
edu_levl	Yes	Education Level of Household Head	Categorical		1-15, 90,91,92		Might need restructuring to simpler groups KG, Primary, Secondary, Intermediat, UG, Masters
bank_acc	Yes	Does household head have a bank account?	Categorical		1-2
poseq_shel	Yes	Household’s shelter condition after earthquake	Categorical		1-5
respreq	Yes	Household’s residence before earthquake	Categorical	yes	1-3
resposq	Yes	Household’s residence after earthquake	Categorical	yes	1-3
rahat1	Yes	type of earthquake victim id card received	Categorical		1-6	Looks like it/ have to confirm with PUJAN
rahat2	Yes	type of earthquake victim id card received	Categorical		1-6	Looks like it/ have to confirm with PUJAN
rahat3	Yes	type of earthquake victim id card received	Categorical		1-6	Looks like it/ have to confirm with PUJAN
rahat4	Yes	type of earthquake victim id card received	Categorical		1-6	Looks like it/ have to confirm with PUJAN
rahat5	Yes	type of earthquake victim id card received	Categorical		1-6	Looks like it/ have to confirm with PUJAN
eqid_typ	Yes	Earthquake ID Type	Categorical		1-4
income	Yes	Household’s average monthly income	Categorical		1-5
hhd_size	No		Numerical			Looks like the size of the household
death	Yes	Has anybody died after earthquake	Categorical		1-2
death_cn	No		?		1,2	Looks like thr count of people who died post earthquake
loss	Yes	Has anybody been badly injured/missing after the earthquage	Categorical		1,2
loss_cn	No		?			Looks like thr count of people who wen missing post earthquake
edrop_tf	Yes	Has anyone left school PE?	Categorical		1,2,3	Data has only twos and threes, no 1s
edrop_cn	No		?			Looks like thr count of people who dropped educ. post earthquake
pdrop_tf	Yes	Has anyone left pregnancy checkup PE?	Categorical		1,2,3
pdrop_cn	No		?			Looks like thr count of people who dropped pregnancy checkups. post earthquake
vdrop_tf	Yes	Has family stopped getting children vaccnated PE?	Categorical		1,2,3
vdrop_cn	No		?			Looks like thr count of children who stopped getting vaccination checkups. post earthquake
oc_ch_tf	Yes	Has anyone had to change their occupation PE?	Categorical		1,2,3
oc_ch_cn	No		?			Looks like thr numper of people who changed occupation post earthquake
h2o_pre	Yes	Main Source of Water before earthquake	Categorical	yes	1-7
h2o_pos	Yes	Main Source of Water post earthquake	Categorical	yes	1-7
fir_pre	Yes	Main source of fuel before earthquake	Categorical	yes	1-6
fir_pos	Yes	Main Source of fuel post earthquake	Categorical	yes	1-6
lit_pre	Yes	Main source of light before earthquake	Categorical	yes	1-6
lit_pos	Yes	Main Source of light post earthquake	Categorical	yes	1-6
toilet_pre	Yes	Type of toilet (pre)	Categorical	yes	1-4
toilet_pos	Yes	Type of toilet (post	Categorical	yes	1-4
ast_pre1	Yes	Fixed assets (pre)	Categorical	yes	1-12	yes
ast_pre2	Yes	Fixed assets (pre)	Categorical	yes	1-12	yes
ast_pre3	Yes	Fixed assets (pre)	Categorical	yes	1-12	yes
ast_pre4	Yes	Fixed assets (pre)	Categorical	yes	1-12	yes
ast_pre5	Yes	Fixed assets (pre)	Categorical	yes	1-12	yes
ast_pre6	Yes	Fixed assets (pre)	Categorical	yes	1-12	yes
ast_pre7	Yes	Fixed assets (pre)	Categorical	yes	1-12	yes
ast_pre8	Yes	Fixed assets (pre)	Categorical	yes	1-12	yes
ast_pre9	Yes	Fixed assets (pre)	Categorical	yes	1-12	yes
ast_pre10	Yes	Fixed assets (pre)	Categorical	yes	1-12	yes
ast_pre11	Yes	Fixed assets (pre)	Categorical	yes	1-12	yes
ast_pre12	Yes	Fixed assets (pre)	Categorical	yes	1-12	yes
ast_pos1	Yes	Fixed assets (post)	Categorical	yes	1-12	yes
ast_pos2	Yes	Fixed assets (post)	Categorical	yes	1-12	yes
ast_pos3	Yes	Fixed assets (post)	Categorical	yes	1-12	yes
ast_pos4	Yes	Fixed assets (post)	Categorical	yes	1-12	yes
ast_pos5	Yes	Fixed assets (post)	Categorical	yes	1-12	yes
ast_pos6	Yes	Fixed assets (post)	Categorical	yes	1-12	yes
ast_pos7	Yes	Fixed assets (post)	Categorical	yes	1-12	yes
ast_pos8	Yes	Fixed assets (post)	Categorical	yes	1-12	yes
ast_pos9	Yes	Fixed assets (post)	Categorical	yes	1-12	yes
ast_pos10	Yes	Fixed assets (post)	Categorical	yes	1-12	yes
ast_pos11	Yes	Fixed assets (post)	Categorical	yes	1-12	yes
ast_pos12	Yes	Fixed assets (post)	Categorical	yes	1-12	yes
respreqd	No						All values are Nas
resposqd	No