res_weights_ext: Simulation (fractional weight) results of health resilience...

In philmikejones/thesis: hrsim: A spatial microsimulation analysis of health inequalities and health resilience

Description

Data frame of fractional weights from resilience spatial microsimulation for health conditions at ouput area level.

Usage

res_weights_ext

Format

The data frame contains the following variables:

code

Doncaster output area code

total

Simulated total population

car_0

count by car ownership

car_1

count by car ownership

car_2_plus

count by car ownership

ten_owned_outright

count by tenure

ten_owned_mortgage_shared

count by tenure

ten_rented

count by tenure

qual_0

count by highest qualification

qual_2

count by highest qualification

qual_3

count by highest qualification

qual_4_plus

count by highest qualification

qual_other

count by highest qualification

mar_civil_part

Marital status: civil partnership

mar_divorced

Marital status: divorced

mar_married

Marital status: married

mar_separated

Marital status: separated

mar_single

Marital status: single (never married)

mar_widowed

Marital status: Widowed or widower

eca_emp

Employed

eca_homefam

Looking after home or family

eca_ltsick

Long-term sick or disabled

eca_other

Other economic activity

eca_retired

Retired

eca_selfemp

Self-employed

eca_student

Student

eca_unemp

Unemployed (looking for work or ready to start job)

sex_female

count by sex

sex_male

count by sex

eth_asian_asian_british

count by ethnicity

eth_black_african_caribbean_british

count by ethnicity

eth_british

count by ethnicity

eth_irish

count by ethnicity

eth_mixed_multiple_ethnic

count by ethnicity

eth_other_ethnicity

count by ethnicity

eth_other_white

count by ethnicity

age_16_17

count by age

age_18_19

count by age

age_20_24

count by age

age_25_29

count by age

age_30_44

count by age

age_45_59

count by age

age_60_64

count by age

age_65_74

count by age

age_75_84

count by age

age_85_89

count by age

age_90_plus

count by age

depress_no

No depression

depress_yes

Clinical depression

ncoh_no

Neighbourhood cohesion: no

ncoh_yes

Neighbourhood cohesion

trust_no

Neighbourhood trust: no

trust_yes

Neighbourhood trust

belong_no

Do not belong to neighbourhood

belong_yes

Belong to neighbourhood

fin_bad

Subjective financial situation: not good

fin_good

Subjective financial situation: managing or comfortable

ghq_conc_bad

GHQ concentration: bad

ghq_conc_good

GHQ concentration: good

ghq_sleep_bad

GHQ sleep loss: bad

ghq_sleep_good

GHQ sleep loss: good

ghq_useful_bad

GHQ play useful role: bad

ghq_useful_good

GHQ play useful role: good

ghq_decision_bad

GHQ can make decisions: bad

ghq_decision_good

GHQ can make decisions: good

ghq_strain_bad

GHQ constantly under strain: bad

ghq_strain_good

GHQ constantly under strain: good

ghq_diff_bad

GHQ problems overcoming difficulties: bad

ghq_diff_good

GHQ problems overcoming difficulties: good

ghq_enjoy_bad

GHQ enjoy day-to-day activities: bad

ghq_enjoy_good

GHQ enjoy day-to-day activities: good

ghq_face_bad

GHQ able to face problems: bad

ghq_face_good

GHQ able to face problems: good

ghq_unhappy_bad

GHQ unhappy or depressed: bad

ghq_unhappy_good

GHQ unhappy or depressed: good

ghq_confid_bad

GHQ losing confidence: bad

ghq_confid_good

GHQ losing confidence: good

ghq_worth_bad

GHQ believe worthless: bad

ghq_worth_good

GHQ believe worthless: good

ghq_happy_bad

GHQ general happiness: bad

ghq_happy_good

GHQ general happiness: good

alcohol_high

Alcohol consumption: high risk(>14 units/week)

alcohol_low

Alcohol consumption: low risk (<=14 units/week)

Source

Spatial microsimulation from 2011 Census Tables from Nomisweb and Understanding Society.

philmikejones/thesis documentation built on May 31, 2019, 2:50 p.m.