ADPTV_CPCTY_R NOTEBOOK.Rmd

---
title: "Building an Adaptive Capacity Index Using TidyCensus in R"
author: "Anthony Holmes - Prepared on Behalf of The Nature Conservancy - Oregon"
copyright: 2020 - The Nature Conservancy
date: "March, 2020"
output:
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    toc: true
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    theme: journal
editor_options: 
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---

## Introduction

According to the CDC, Social vulnerability refers to the "resilience of
communities when confronted by external stresses on human health,
stresses such as natural or human-caused disasters, or disease
outbreaks." These methods implement an index similar to the one
maintained by the CDC which are adapted from methods published by
Flanagan et al. (2011) in "A Social Vulnerability Index for Disaster
Management," which describes 13 metrics for assessing the 'social
vulnerability' or 'adaptive capacity' (Davies et al. 2018) of a census
tract. This study attempts to replicate these metrics as closely as
possible at a block group scale using 5 year ACS data in order to
quantify the capacity of a block-group to respond to a given
disturbance, minimizing risks to health, safety, property and other
essential services. This index could be used to identify areas and
communities at an increased risk and target appropriate response,
recovery and mitigation efforts.

The SVI/ACI is **just one** component of a vulnerability assessment
which would include assessing the impact of the hazard itself (disease,
fire, flood hurricane, etc.), the vulnerability of the physical
infrastructure, and community assets/resources that could diminish the
impact of a given hazard and/or aid in recovery efforts in a given
community (Flanagan et al. 2011).

The index comprises 4 domains including:

> **Socioeconomic Status**
>
> -   Percent of Persons Below Poverty Level
>
> -   Percent of Persons (age 16+) Unemployed
>
> -   Per Capita Income

> **Language & Education**
>
> -   Percent of Persons With No 12th Grade Education
>
> -   Percent of Persons Do Not Who Speak English

> **Demographics**
>
> -   Percent of Persons 65 Years of Age or Older
>
> -   Percent of Persons 17 Years of Age or Younger
>
> -   Percent of Persons 5 Years of Age or Older With a Disability
>
> -   Percent of Single Parent Households

> **Housing & Transportation**
>
> -   Percent of Persons Living in Multi-Unit Structure
>
> -   Percent of Persons Living in a Mobile Home
>
> -   Percent of Persons Living in 'Crowded' Conditions (more than one
>     person per room)
>
> -   Percent of Household With No Vehicle Available
>
> -   Percent of Persons Residing in Group Quarters

The index is constructed according to Flanagan et al. (2011) by
assigning a percentile rank to each of the above named variables, ranked
from highest to lowest (excluding per capita income which is ranked from
lowest to highest). To calculate the final SVI, the sum for each of the
previously calculated percentile ranks is taken for a given blockgroup
and the percentile rank of these sums across a given geographic area
results in the final SVI score, accordingly, the geographic area and
enumeration units used in this equation will effect the final SVI
derived for a given area so it is advised that the user consider these
factors carefully.

Additional variables were brought in based on a review of the existing
literature, however, these were not included in the index in this script
since we were attempting to implement the existing literature of
Flanagan et al. as closely as possible, however, the literature cited
here supports their application in future iterations of the SVI index
and their contribution to social vulnerability is worthy of further
review.

## Works Cited

-   Davies, Ian P., Haugo, Ryan D., Robertson, James C., & Levin,
    Phillip S. (2018). The unequal vulnerability of communities of color
    to wildfire. PLoS ONE, 13(11), E0205825.
    <https://doi.org/10.1371/journal.pone.0205825>

-   Flanagan BE, Gregory EW, Hallisey EJ, Heitgerd JL, Lewis B. A Social
    Vulnerability Index for Disaster Management. J Homel Secur Emerg
    Manag. 2011;8. <https://doi.org/10.2202/1547-7355.1792>

## Tidy Census

This script implements the R Package TidyCensus to bring in the
necessary variables for this index, this allows the index to be easily
reproduced for a variety of geographies and time periods, many of the
variables were found at the block group level and some could not be
derived at this scale, there may be alternative variables or indicators
available, however, this project only attempts to reproduce the index as
published at the finest geographic scale available.

-   Kyle Walker (2020). tidycensus: Load US Census Boundary and
    Attribute Data as 'tidyverse' and 'sf'-Ready Data Frames. R package
    version 0.9.6. <https://CRAN.R-project.org/package=tidycensus>

## Block Group Level

These were the variables available/derived at the block group level

> -   **Total Population**
>
> -   **Percent of Population with No Vehicle Available**
>
> -   **Percent of Population Unemployed**
>
> -   **Percent of Population Speak English Less Than "Very Well"**
>
> -   **Percent of Population Living in Mobile Homes**
>
> -   **Percent of Population Living in Accommodations W/ Less Than 1
>     Room Per Person (Crowding)**
>
> -   **Percent of Population 65+**
>
> -   **Percent of Population 65+**
>
> -   **Percent of Population 25+ Less Than Hs Graduate**
>
> -   **Percent of Population 17 Years and Under**
>
> -   **Percent of Housing Units W/ 10+ Units in Structure**
>
> -   **Percent Minority**
>
> -   **Per Capita Income**

### Blockgroup Level Variables

These are all the variables used in the calculations at the blockgroup
level

```{r}
c('B25003_001','B25003_003','B25070_007','B25070_008','B25070_009','B25070_010','B25071_001','B11007_001','B11007_003','B25034_001','B25034_008','B25034_009','B25034_01','B25034_011','B01003_001','B19301_001','B25033_001','B25033_006','B25033_007','B25033_012','B25033_013','B25044_001','B25044_003','B25044_010','B23025_003','B23025_005','B25014_001','B25014_005','B25014_006','B25014_007','B25014_011','B25014_012','B25014_013','B25024_001','B25024_007','B25024_008','B25024_009','B09021_022','B09021_001','B01001_020','B01001_021','B01001_022','B01001_023','B01001_024','B01001_025','B01001_044','B01001_045','B01001_046','B01001_047','B01001_048','B01001_049','B99163_001','B99163_005','B01001_003','B01001_004','B01001_005','B01001_006','B01001_027','B01001_028','B01001_029','B01001_030','B03002_003','B02001_004','B02001_00','B02001_003','B03003_003','B02001_006','B02001_007','B02001_008','B03002_003','B03002_001','B02001_001','B25002_001','B25002_003','B15003_001','B15003_016','B15003_017','B15003_018','B15003_019','B15003_020','B15003_021','B15003_022','B15003_023','B15003_024','B15003_025','B23008_008', 'B23008_021', 'B23008_002', 'B23008_015')
```

### How The Values Were Calculated

#### **Total Population**

    B01003_001

#### **Per Capita Income**

      B19301_001 - Estimate!!Per capita income in the past 12 months (in 2018 inflation-adjusted dollars)

#### **Percent of Population Below Poverty Level**

      (C17002_002+C17002_003)/C17002_001

    # C17002_001 - Estimate!!Total - Ratio of Income to Poverty in the Last 12 Months 
    # C17002_002 - Estimate!!Total!!Under .50 
    # C17002_003 - Estimate!!Total!!.50 to .99

#### **Percent of Population 25+ with Less than a 12th Grade Education**

      1 - (B15003_016+B15003_017+B15003_018+B15003_019+B15003_020+B15003_021+B15003_022+B15003_023+B15003_024+B15003_025)/B15003_001

    # B15003_001 - Estimate!!Total 
    # B15003_016 - Estimate!!Total!!12th grade, no diploma # B15003_017 - Estimate!!Total!!Regular high school diploma 
    # B15003_018 - Estimate!!Total!!GED or alternative credential 
    # B15003_019 - Estimate!!Total!!Some college, less than 1 year 
    # B15003_020 - Estimate!!Total!!Some college, 1 or more years, no degree #B15003_021 - Estimate!!Total!!Associate's degree 
    # B15003_022 - Estimate!!Total!!Bachelor's degree 
    # B15003_023 - Estimate!!Total!!Master's degree 
    # B15003_024 - Estimate!!Total!!Professional school degree 
    # B15003_025 - Estimate!!Total!!Doctorate degree

#### **Percent of Population Living in Mobile Homes - Mobile Homes Estimate**

      (B25033_006+B25033_007+B25033_012+B25033_013)/B25033_001

    # B25033_001 - Estimate!!Total #B25033_006 - Estimate!!Total!!Owner occupied!!Mobile home 
    # B25033_007 - Estimate!!Total!!Owner occupied!!Boat, RV, van, etc. # B25033_012 - Estimate!!Total!!Renter occupied!!Mobile home 
    # B25033_013 - Estimate!!Total!!Renter occupied!!Boat, RV, van, etc.

#### **Percent of Population with No Vehicle Available - Households With No Vehicle Available Estimate**

      (B25044_003+B25044_010)/B25044_001

    # B25044_001 - Estimate!!Total 
    # B25044_003 - Estimate!!Total!!Owner occupied!!No vehicle available # B25044_010 - Estimate!!Total!!Renter occupied!!No vehicle available

#### **Percent of Population Unemployed - Civilian (Age 16+) Unemployed Estimate**

      B23025_005/B23025_003

    # B23025_003 - Estimate!!Total!!In labor force!!Civilian labor force # B23025_005 - Estimate!!Total!!In labor force!!Civilian labor force!!Unemployed

> **Notes:** According to the Census Bureau "In Civilian Labor Force"
> includes all civilians 16 years older.

#### **Percent of Population Living In Accommodations with Less Than 1 Room Per Person/Crowding - At Household Level, Occupied Housing Units, More People Than Rooms Estimate**

      (B25014_005+B25014_006+B25014_007+B25014_011+B25014_012+B25014_013)/B25014_001

    # B25014_001 - Estimate!!Total 
    # B25014_005 - Estimate!!Total!!Owner occupied!!1.01 to 1.50 occupants per room 
    # B25014_006 - Estimate!!Total!!Owner occupied!!1.51 to 2.00 occupants per room 
    # B25014_007 - Estimate!!Total!!Owner occupied!!2.01 or more occupants per room 
    # B25014_011 - Estimate!!Total!!Renter occupied!!1.01 to 1.50 occupants per room 
    # B25014_012 - Estimate!!Total!!Renter occupied!!1.51 to 2.00 occupants per room 
    # B25014_013 - Estimate!!Total!!Renter occupied!!2.01 or more occupants per room

#### **Percent of Housing Units with 10+ Units in Structure - Housing in Structures With 10 or More Units Estimate**

      (B25024_007+B25024_008+B25024_009)/B25024_001

    # B25024_001 - Estimate!!Total 
    # B25024_007 - Estimate!!Total!!10 to 19 
    # B25024_008 - Estimate!!Total!!20 to 49 
    # B25024_009 - Estimate!!Total!!50 or more

#### **Percent Of Population 65+ - Persons Aged 65 And Older Estimate**

      B09021_022/B09021_001

    # B09021_022 - Estimate!!Total!!65 years and over 
    # B09021_001 - Estimate!!Total

#### **Percent Of Population Who Do Not Speak English**

      B99163_005/B99163_001

    # B99163_001 - Estimate!!Total 
    # B99163_005 - Estimate!!Total!!Speak other languages!!Ability to speak English –!!Not allocated

> **Notes:** According to Flanagan et al. (2011) this should be "percent
> of persons who speak English "less than well," however, a suitable
> variable could not be found to represent this in the 2018 ACS, that is
> not to say one doesn't exist.

#### **Percent of Population 17 Years of Age and Under**

      (B01001_003+B01001_004+B01001_005+B01001_006+B01001_027+B01001_028+B01001_029+B01001_030)/B01003_001

    # B01001_003 - Estimate!!Total!!Male!!Under 5 years 
    # B01001_004 - Estimate!!Total!!Male!!5 to 9 years 
    # B01001_005 - Estimate!!Total!!Male!!10 to 14 years 
    # B01001_006 - Estimate!!Total!!Male!!15 to 17 years 
    # B01001_027 - Estimate!!Total!!Female!!Under 5 years 
    # B01001_028 - Estimate!!Total!!Female!!5 to 9 years 
    # B01001_029 - Estimate!!Total!!Female!!10 to 14 years 
    # B01001_030 - Estimate!!Total!!Female!!15 to 17 years

#### **Percent Minority**

      1-(B03002_003/B03002_001)

    # B03002_003 - Estimate!!Total (WHITE ALONE, NOT HISPANIC OR LATINO)

> **Notes:** I used the B03002 table here because "Hispanic/Latino" is
> not considered a "race" by the U.S. Census Bureau, people of Hispanic
> or Latino origin either don't answer this question or they
> select/write-in any number of racial categories and which one they
> select can vary by geography and other demographic factors. According
> to the Pew Research Center, "In the 2010 census, 37% of Hispanics -
> 18.5 million people - said they belonged to "some other race." Among
> those who answered the race question this way in the 2010 census,
> 96.8% were Hispanic. And among those Hispanics who did, 44.3%
> indicated on the form that Mexican, Mexican American or Mexico was
> their race, 22.7% wrote in Hispanic or Hispano or Hispana, and 10%
> wrote in Latin American or Latino or Latin." The Census Bureau
> suggests that if you want to treat "Hispanic" as a "race-like"
> category, you use the B03002 table. So if you were to use the B02001
> table for "Percent White", it could also include minority Hispanic
> groups that identify as white.
>
> **Sources:**
>
> -   [\<https://censusreporter.org/topics/race-hispanic/\>](https://censusreporter.org/topics/race-hispanic/){.uri}
>
> -   [\<https://www.pewsocialtrends.org/2015/06/11/chapter-1-race-and-multiracial-americans-in-the-u-s-census/\>](https://www.pewsocialtrends.org/2015/06/11/chapter-1-race-and-multiracial-americans-in-the-u-s-census/){.uri}

### **Optional Variables (Not Used in SVI)**

#### **Percent of Homes Occupied**

      1-B25002_003/B25002_001

    # B25002_001 - Estimate!!Total (TOTAL NUMBER OF HOUSING UNITS) 
    # B25002_003 - Estimate!!Total!!Vacant

#### **Percent of Homes Renter Occupied**

      B25003_003/B25003_001

    # B25003_001 - Estimate!!Total B25003_003 - Estimate!!Total!!Renter occupied

> **Notes:** According to Lee et al. 2019, "Previous studies have shown
> that renters lack resources prior to a disaster (preparedness) and
> continue to do so in post disaster times (recovery). Before a
> disaster, available resources differ, including available funds and
> housing condition and location. Renters have limited household,
> social, and physical resources prior to a disaster, as compared to
> those of owners. Additionally, renters tend to suffer more severe
> damage during a disaster. During recovery, renters limited financial
> resources (i.e., lack of insurance and less governmental assistance;
> Bolin and Stanford 1998; Comerio 1998), as well as inadequate social
> and political capital, mean that they have less influence on decisions
> being made about recovery (Morrow 1999). This causes them to face
> greater struggles over a longer period of time." (Lee et al. 2019)
>
> **Sources:**
>
> -   Lee, J., & Van Zandt, S. (2019). Housing Tenure and Social
>     Vulnerability to Disasters: A Review of the Evidence. Journal of
>     Planning Literature, 34(2), 156-170.

#### **Percent of Households Paying Mote than 30% of Their Income on Rent**

      (B25070_007+B25070_008+B25070_009+B25070_010)/b25070_001

    # B25070_007 - Estimate!!Total!!30.0 to 34.9 percent 
    # B25070_008 - Estimate!!Total!!35.0 to 39.9 percent 
    # B25070_009 - Estimate!!Total!!40.0 to 49.9 percent 
    # B25070_010 - Estimate!!Total!!50.0 percent or more

#### **Median Gross Rent as a Percentage of Income**

      B25071_001

    # B25071_001 - Estimate!!Median gross rent as a percentage of household income

> **Notes:** According to the U.S. Department of Housing and Urban
> Development (HUD) "Families who pay more than 30 percent of their
> income for housing are considered cost burdened and may have
> difficulty affording necessities such as food, clothing,
> transportation and medical care." They may be above poverty level, but
> the cost of housing may still be a factor that effects their
> vulnerability and ability to recover from a crisis (HUD). According to
> Diaz et al., "Renting, crowding, and unaffordable housing are directly
> and indirectly linked with negative outcomes for children and adults
> (e.g., Evans, Lepore, Shejwal, & Palsane, 1998; Haurin, Parcel, &
> Haurin, 2002; Leventhal & Newman, 2010; Pollack, Griffın, & Lynch,
> 2010). These housing situations, coupled with other vulnerabilities,
> also increase exposure to harm and limit the ability of individuals
> and households to cope with and recover from the impacts of
> environmental hazards (...) For many households, spending more on
> housing costs than is standard suggests housing situations that are
> unstable, difficult to maintain over the long term, and may reflect
> stresses that are otherwise difficult to detect" (excerpt from Diaz et
> al. 2017).
>
> **Sources:**
>
> -   Díaz Mcconnell, E. (2017). Rented, Crowded, and Unaffordable?
>     Social Vulnerabilities and the Accumulation of Precarious Housing
>     Conditions in Los Angeles. Housing Policy Debate, 27(1), 60-79.

#### **Percent of Households with Seniors 65+ Living Alone**

      B11007_003/B11007_001

    # B11007_001 - Estimate!!Total 
    # B11007_003 - Estimate!!Total!!Households with one or more people 65 years and over!!1-person household

> **Notes:** "That seniors are more vulnerable to disasters is a
> proposition that is supported by a growing body of literature.
> Experimental research confirms that the elderly and disabled confront
> unique difficulties during periods of evacuation." (Donner et al.
> 2008). For example: "Half of the deaths from Hurricane Katrina were
> adults age 75 and older (Brunkard, Namulanda, and Ratard, 2008), and
> 63 percent of the deaths after the 1995 heat wave in Chicago were
> adults age 65 or older (Whitman et al. 1997)" ... A 2012 survey found
> that 15 percent of U.S. adults age 50 or older would not be able to
> evacuate their homes without help, and half of this group would need
> help from someone outside the household (National Association of Area
> Agencies on Aging, National Council on Aging, and UnitedHealthcare,
> 2012) (Shih et al. 2018)". According to Chau et al. "Living alone
> increases the risk of social isolation, which may, in turn, be
> associated with poorer mental and physical health, and leads to
> problems in escape and recovery from emergency situations" (Chau et
> al. 2014).
>
> **Sources:**
>
> -   Chau, P., Gusmano, H., Cheng, M., Cheung, K., & Woo, J. (2014).
>     Social Vulnerability Index for the Older People---Hong Kong and
>     New York City as Examples. Journal of Urban Health, 91(6),
>     1048-1064.
>
> -   Shih RA, Acosta JD, Chen EK, et al. Improving Disaster Resilience
>     Among Older Adults: Insights from Public Health Departments and
>     Aging-in-Place Efforts. Rand Health Q. 2018;8(1):3. Published 2018
>     Aug 2.

#### **Percent of Homes Built Before 1969**

      (B25034_008+B25034_009+B25034_010+B25034_011)/B25034_001

    # B25034_001 - Estimate!!Total 
    # B25034_008 - Estimate!!Total!!Built 1960 to 1969 
    # B25034_009 - Estimate!!Total!!Built 1950 to 1959 
    # B25034_010 - Estimate!!Total!!Built 1940 to 1949 
    # B25034_011 - Estimate!!Total!!Built 1939 or earlier

> **Notes:** "Congress created the National Flood Insurance Program
> (NFIP) in 1968 In order to participate, jurisdictions are required to
> adopt a floodplain management ordinance. Units that pre-date the local
> adoption of those ordinances are more likely to be out of compliance
> with the design and construction standards they require. Thus, the age
> of the buildings in floodplains may offer some insight into whether
> buildings are designed or retrofitted for flooding." (Furman Center)

## Tract Level

These Variables were only found to be available at the Tract level, that
is not to say a suitable variable does not exist at the block-group
level

> -   **Percent of Population Below Poverty Level**
>
> -   **Percent of Population 5\< with a Disability**
>
> -   **Percent of Population Living in Group Quarters**
>
> -   **Percent of Children Living in Single Parent Households**

### Tract Level Variables

```{r}
c('B18101_025','B18101_026','B18101_006','B18101_007','C18130_009','C18130_010','C18130_016','C18130_017','B17020_001','B17020_002','B26001_001','B11001_001','B11004_012','B11004_018','B11001_001','B09008_010','B09008_011','B09008_012','B17023_001','B17023_016','B17023_017','B17023_018')
```

### How The Values Were Calculated

#### **Percent of Population 5\< with a Disability - Civilian Non-institutionalized Population With A Disability Estimate)**

      (B18101_026+B18101_007+C18130_010+C18130_017)/(B18101_025+B18101_006+C18130_009+C18130_016)

    # B18101_025 - Estimate!!Total!!Female!!5 to 17 years 
    # B18101_026 - Estimate!!Total!!Female!!5 to 17 years!!With a disability 
    # B18101_006 - Estimate!!Total!!Male!!5 to 17 years 
    # B18101_007 - Estimate!!Total!!Male!!5 to 17 years!!With a disability 
    # C18130_009 - Estimate!!Total!!18 to 64 years 
    # C18130_010 - Estimate!!Total!!18 to 64 years!!With a disability 
    # C18130_016 - Estimate!!Total!!65 years and over 
    # C18130_017 - Estimate!!Total!!65 years and over!!With a disability

#### **Percent of Population Living in Group Quarters**

      B26001_001/B01003_001

    # B26001_001 - Estimate!!Total!!Group quarters population 

#### **Percent of Children Living in Single Parent Households**
    
      (B23008_008+B23008_021)/(B23008_002+B23008_015)

    #B23008_008 - Estimate!!Total!!Under 6 years!!Living with one parent
    #B23008_021 - Estimate!!Total!!6 to 17 years!!Living with one parent
    #B23008_002 - Estimate!!Total!!Under 6 years
    #B23008_015 - Estimate!!Total!!6 to 17 years
    
    **OR**
    
      (B09008_010+B09008_011+B09008_012)/B09008_001

    # B09008_001 - Estimate!!Total 
    # B09008_010 - Estimate!!Total!!No unmarried partner of householder present!!In family households!!In male householder, no wife present, family 
    # B09008_011 - Estimate!!Total!!No unmarried partner of householder present!!In family households!!In female householder, no husband present, family 
    # B09008_012 - Estimate!!Total!!No unmarried partner of householder present!!In nonfamily households

> **Notes:** According to Flanagan et al. this should be an estimate of
> Percent male or female householder, no spouse present, with children
> under 18. There may be a variety of approximations available,
> including, but not limited to B23008; however, these tables fails to
> represent a variety of housing situations in which a child may be
> under the guardianship of a single adult caretaker and may also
> include situations in which a household is classified as a single
> parent household where there is the presence of an unmarried partner
> who is likely to provide support in the responsibilities of parenting,
> diminishing the unique risk faced by a single parent in a disaster
> scenario. According the Census Bureau, "A nonfamily householder" is a
> "householder living alone or with non-relatives only." A family is a
> group of two people or more (one of whom is the householder) related
> by birth, marriage, or adoption and residing together so If there is
> 1.) No "unmarried partner of householder present" and 2.) It is a
> nonfamily household so the absence of a married partner is assumed and
> 3.) There is a child present then I assumed it would include children
> living with unmarried non-relatives where there is no unmarried
> partner present so although the child is unrelated to their adult
> caretaker, their adult care taker would function essentially as a
> "single parent."
>
> Users may compare this estimate with those of B23008 to determine
> which is more appropriate in their area of study, if the difference in
> these estimates is not significant it may be preferable to use B23008
> since this can be calculated at the block group scale.

### **Optional Variables (Not Used in SVI)**

#### Percent Single Mother Households Below Poverty Line

      (B17023_016+B17023_017+B17023_018)/B22002_001

    # B17023_001 - Estimate!!Total 
    # B17023_016 - Estimate!!Total!!Income in the past 12 months below poverty level!!Other families!!Female householder, no husband present!!1 or 2 own children of the householder 
    # B17023_017 - Estimate!!Total!!Income in the past 12 months below poverty level!!Other families!!Female householder, no husband present!!3 or 4 own children of the householder 
    # B17023_018 - Estimate!!Total!!Income in the past 12 months below poverty level!!Other families!!Female householder, no husband present!!5 or more own children of the householder

> **Notes:** "Demographic data shows that single mothers tend to be less
> educated and poorer than the general population, thus placing them at
> greater risk to disasters (...) Morrow and Enarson (1996) observed,
> during the long-term recovery period following Hurricane Andrew, that
> poor and minority women experienced significant difficulties in
> accessing relief and recovery aid given that policy programs were
> reportedly set up only with small, nuclear households in mind.
> Moreover, Morrow and Enarson argue that women were victims of
> exploitation and fraud during the recovery period. In another study,
> Donner (2003) shows that census tracts in the United States with
> larger percentages of single mothers are more likely to experience a
> tornado death and/or injury. Rodriguez and Russell (2006) also report
> that women and children were disproportionately affected by the 2004
> Indian Ocean Tsunami and were more likely to be victims (e.g.,
> fatalities) of this catastrophic event relative to their male
> counterparts." (excerpt from Donner et al. 2008)
>
> **Sources:** Donner, W., & Rodríguez, H. (2008). Population
> Composition, Migration and Inequality: The Influence of Demographic
> Changes on Disaster Risk and Vulnerability. Social Forces, 87(2),
> 1089-1114. Retrieved February 20, 2020, from
> www.jstor.org/stable/20430904

## R

**Install and load required packages**

```{r}
    ReqPkgs <- c('knitr','sp','sf','spdep','tidycensus','dplyr','tidyr','mapview','RColorBrewer','leaflet','leafpop','ggplot2')
    ReqPkgs <- as.list(ReqPkgs)
    #suppressMessages(lapply(ReqPkgs, install.packages, character.only = TRUE))
    suppressMessages(lapply(ReqPkgs, require, character.only = TRUE))
```

### TidyCensus

> For this section you will use the `tidycensus` package to read in data
> from the American Community Survey, including geometry.

Get a Free Census Api Key Here:
<https://api.census.gov/data/key_signup.html>

```{r}
    #tidycensus::census_api_key(key = 'YOUR CENSUS API KEY HERE', install = TRUE, overwrite = TRUE)
    readRenviron("~/.Renviron")
```

### List Counties in State

> Now were making a simple character vector to store the names of all
> the counties we'll be using to pull in the census data since no option
> exists to pull out blockgroup level data for the whole state.

```{r}
Counties <- tigris::list_counties(state = 'Oregon')
Counties <- Counties$county
print(Counties)
```

### List Variables To Be Pulled In

> Now were making a simple character vector to store the names of all
> the variables we'll be pulling in at the blockgroup and tract level,
> these were selected through a long process of trial an error

```{r}
#Blockgroup Level Variables
varsBG <- c('B25003_001','B25003_003','B25070_007','B25070_008','B25070_009','B25070_010','B25071_001','B11007_001','B11007_003','B25034_001','B25034_008','B25034_009','B25034_010','B25034_011','B01003_001','B19301_001','B25033_001','B25033_006','B25033_007','B25033_012','B25033_013','B25044_001','B25044_003','B25044_010','B23025_003','B23025_005','B25014_001','B25014_005','B25014_006','B25014_007','B25014_011','B25014_012','B25014_013','B25024_001','B25024_007','B25024_008','B25024_009','B09021_022','B09021_001','B01001_020','B01001_021','B01001_022','B01001_023','B01001_024','B01001_025','B01001_044','B01001_045','B01001_046','B01001_047','B01001_048','B01001_049','B99163_001','B99163_005','B01001_003','B01001_004','B01001_005','B01001_006','B01001_027','B01001_028','B01001_029','B01001_030','B03002_003','B02001_004','B02001_001','B02001_003','B03003_003','B02001_006','B02001_007','B02001_008','B03002_003','B03002_001','B02001_001','B25002_001','B25002_003','B15003_001','B15003_016','B15003_017','B15003_018','B15003_019','B15003_020','B15003_021','B15003_022','B15003_023','B15003_024','B15003_025','B02001_005','B03003_001','B25070_001','B17020_001','C17002_001','C17002_002','C17002_003','C17002_004', 'B23008_008', 'B23008_021', 'B23008_002', 'B23008_015', 'B23008_008', 'B23008_021', 'B23008_002', 'B23008_015')

#Tract Level Variables
varsCT <- 
c('B01003_001','B18101_025','B18101_026','B18101_006','B18101_007','C18130_009','C18130_010','C18130_016','C18130_017','B26001_001','B11004_012','B11004_018','B09008_001','B09008_010','B09008_011','B09008_012','B17023_001','B17023_016','B17023_017','B17023_018','B22002_001')
```

### Pulling in Block Group Level

> This chunk of code is pulling in all the blockgroup level variables
> described previously

```{r}
 CBG18_1 <- tidycensus::get_acs(

#get_decentennial() pulls in data from the decentennial census 1990-2010
  
  geography = 'block group', #other options include us, region, division, state, county subdivision, census tract, block, place, alaska native regional corporation, american indian area/alaska native area/hawaiian home land, american indian area/alaska native area (reservation or statistical entity only), american indian area (off-reservation trust land only)/hawaiian home land, metropolitan statistical area/micropolitan statistical area, combined statistical area, new england city and town area, combined new england city and town area, urban area, congressional district, school district (elementary, secondary or unified), public use microdata area, zip code tabulation area, and state legislative district (upper or lower chamber).
  
  state = 'OR',
  
  county = Counties, #The county list created in the previous step
  
  survey = 'acs5', #could include the ACS 1, 3 or 5 year surveys
  
  year = 2018, #2009 through 2018 are available. Defaults to 2018
  
  variables = varsBG, #The variable list created in the previous step, use tidycensus::load_variables to see what variables are available for the survey and or geography, there may be alternatives or others you want to add!

  geometry = FALSE, #if TRUE, uses the tigris package to return an sf tibble with simple feature geometry in the 'geometry' column. We use Tigris later to pull the geometry in.
  
  output = 'wide',
  
  show_call = FALSE
  
)

#Separate Place Names#

CBG18_1 <- tidyr::separate(data = CBG18_1, col = "NAME", into = c("BLOCK_GROUP","CENSUS_TRACT","COUNTY","STATE"), sep = ",", remove = FALSE)

CBG18_1$TRACT_GEOID <- substring(CBG18_1$GEOID, 1, 11)

print(dim(CBG18_1)) #The dimensions should match this for Oregon: 2,634 x 187
```

### Pulling in Tract Level Variables

> This chunk of code is pulling in all the tract level variables
> described previously

```{r}
CT18B <- tidycensus::get_acs(
  
  geography = 'Tract',
  
  state = 'OR',
  
  county = Counties,

    #c('Columbia','Clatsop','Umatilla','Wallowa','Morrow','Union','Gilliam','Tillamook','Washington','Sherman','Multnomah','Hood River','Wasco','Clackamas','Yamhill','Marion','Baker','Polk','Wheeler','Lincoln','Grant','Jefferson','Linn','Benton','Crook','Malheur','Deschutes','Lane','Harney','Douglas','Lake','Klamath','Coos','Jackson','Curry','Josephine'), # This can include multiple counties
  
  survey = 'acs5',
  
  year = 2018,
  
  variables = varsCT,

  geometry = FALSE,
  
  output = 'wide',
  
  show_call = FALSE
  
)

#Separate Place Names#

CT18B <- tidyr::separate(data = CT18B, col = "NAME", into = c("CENSUS_TRACT","COUNTY","STATE"), sep = ",")

CT18B$TRACT_GEOID <- CT18B$GEOID
names(CT18_1)[names(CT18_1) == "B01003_001E"] <- "B01003_001T" #Giving the variable associated with the population for the census tract a unique name so it can be distinguished from the population of the blockgroup

print(dim(CT18B))

#The dimensions should match this for Oregon: 834 x 45
```

### Join Tables

> This chunk uses dplyr to join the tract level variables to the block
> groups, the variables remain consistent across the block group, this
> is not ideal and if you find some way to represent these variables
> more accurately at the block group level, please feel free to change
> them.

```{r}
JndTbls <- dplyr::left_join(x = CBG18_1, y = CT18B, by = "TRACT_GEOID")

dim(JndTbls) #get dimensions

#The dimensions should match this for Oregon: 2,634 x 231
```

### Now to calculate each of the statistics

```{r}
#SOCIOECONOMIC STATUS:

JndTbls$TOTPOP <- JndTbls$B01003_001E #TOTAL_POPULATION - 
JndTbls$POV <- (JndTbls$C17002_002E+JndTbls$C17002_003E)/JndTbls$C17002_001E #PER_POVERTY
JndTbls$UNEMP <- JndTbls$B23025_005E/JndTbls$B23025_003E #PER_UNEMPLOYED 
JndTbls$PCI <- JndTbls$B19301_001E #PER_CAPITA_INCOME

#LANGUAGE AND EDUCATION:

JndTbls$NOHSDP <- 1-((JndTbls$B15003_016E+JndTbls$B15003_017E+JndTbls$B15003_018E+JndTbls$B15003_019E+JndTbls$B15003_020E+JndTbls$B15003_021E+JndTbls$B15003_022E+JndTbls$B15003_023E+JndTbls$B15003_024E+JndTbls$B15003_025E)/JndTbls$B15003_001E) #PER_LESS_HS_GRAD
JndTbls$LIMENG <-  JndTbls$B99163_005E/JndTbls$B99163_001E #PER_POOR_ENGLISH

#DEMOGRAPHICS:

JndTbls$AGE65 <- JndTbls$B09021_022E/JndTbls$B09021_001E #PER_OVER_65 
JndTbls$AGE17 <- (JndTbls$B01001_003E+JndTbls$B01001_004E+JndTbls$B01001_005E+JndTbls$B01001_006E+JndTbls$B01001_027E+JndTbls$B01001_028E+JndTbls$B01001_029E+JndTbls$B01001_030E)/JndTbls$B01003_001E #PER_UNDER_17 
JndTbls$DISABL <- (JndTbls$B18101_026E+JndTbls$B18101_007E+JndTbls$C18130_010E+JndTbls$C18130_017E)/(JndTbls$B18101_025E+JndTbls$B18101_006E+JndTbls$C18130_009E+JndTbls$C18130_016E) #PER_DISABLED
JndTbls$SNGPNT <- (B23008_008+B23008_021)/(B23008_002+B23008_015) 
#JndTbls$SNGPNT <- (JndTbls$B09008_010E+JndTbls$B09008_011E+JndTbls$B09008_012E)/JndTbls$B09008_001E #PER_SINGL_PRNT #PER_SINGL_PRNT Option 2 (See Notes 496-521)

#HOUSING AND TRANSPORTATION:

JndTbls$MUNIT <- (JndTbls$B25024_007E+JndTbls$B25024_008E+JndTbls$B25024_009E)/JndTbls$B25024_001E #PER_MULTI_DWELL
JndTbls$MOBILE <- (JndTbls$B25033_006E+JndTbls$B25033_007E+JndTbls$B25033_012E+JndTbls$B25033_013E)/JndTbls$B25033_001E #PER_MOBILE_DWEL
JndTbls$CROWD <- (JndTbls$B25014_005E+JndTbls$B25014_006E+JndTbls$B25014_007E+JndTbls$B25014_011E+JndTbls$B25014_012E+JndTbls$B25014_013E)/JndTbls$B25014_001E #PER_CROWD_DWELL
JndTbls$NOVEH <- (JndTbls$B25044_003E+JndTbls$B25044_010E)/JndTbls$B25044_001E #PER_NO_VEH_AVAIL
JndTbls$GROUPQ <- JndTbls$B26001_001E / JndTbls$B01003_001T #PER_GROUP_DWELL

#RACIAL AND ETHNIC MAKEUP:

JndTbls$MINORITY <- 1-(JndTbls$B03002_003E/JndTbls$B03002_001E)
JndTbls$NTVAMRCN <- JndTbls$B02001_004E/JndTbls$B02001_001E
JndTbls$ASIAN <- JndTbls$B02001_005E/JndTbls$B02001_001E
JndTbls$BLACK <- JndTbls$B02001_003E/JndTbls$B02001_001E
JndTbls$HISPLATX <- JndTbls$B03003_003E/JndTbls$B03003_001E
JndTbls$PACISL <- JndTbls$B02001_006E/JndTbls$B02001_001E
JndTbls$OTHRRACE <- JndTbls$B02001_007E/JndTbls$B02001_001E
JndTbls$MULTRACE <- JndTbls$B02001_008E/JndTbls$B02001_001E
JndTbls$WHITE <- JndTbls$B03002_003E/JndTbls$B03002_001E

#OPTIONAL VARIABLES:

JndTbls$HOMESOCCPD <- 1-JndTbls$B25002_003E/JndTbls$B25002_001E
JndTbls$RENTER <- JndTbls$B25003_003E/JndTbls$B25003_001E
JndTbls$RENTBURDEN <- (JndTbls$B25070_007E+JndTbls$B25070_008E+JndTbls$B25070_009E+JndTbls$B25070_010E)/JndTbls$B25070_001E
JndTbls$RENTASPERINCOME <- (JndTbls$B25071_001E/100)
JndTbls$OVR65ALONE <- JndTbls$B11007_003E/JndTbls$B11007_001E
JndTbls$BLTBFR1969 <- (JndTbls$B25034_008E+JndTbls$B25034_009E+JndTbls$B25034_010E+JndTbls$B25034_011E)/JndTbls$B25034_001E
JndTbls$SVRPOV <- JndTbls$C17002_002E/JndTbls$C17002_001E
JndTbls$MODPOV <- JndTbls$C17002_004E/JndTbls$C17002_001E
JndTbls$SINGLMTHRPVRTY <-(JndTbls$B17023_016E+JndTbls$B17023_017E+JndTbls$B17023_018E)/JndTbls$B17023_001E

#RANKING#

#These functions rank each of the variables, variables with matching values across ranks are given the max score, this is the default in excel where the original formulae were derived

a <- JndTbls$RNKPOV <- rank(x = -JndTbls$POV, na.last = "keep", ties.method = "max")
b <- JndTbls$RNKUNEMP <- rank(x = -JndTbls$UNEMP, na.last = "keep", ties.method = "max")
c <- JndTbls$RNKPCI <- rank(x = JndTbls$PCI, na.last = "keep", ties.method = "max") #Note that we are not taking the inverse here because the higher the Per Capita Income, the greater the Adaptive Capacity of a given blockgroup
d <- JndTbls$RNKNOHSDP <- rank(x = -JndTbls$NOHSDP, na.last = "keep", ties.method = "max")
e <- JndTbls$RNKLIMENG <- rank(x = -JndTbls$LIMENG, na.last = "keep", ties.method = "max")
f <- JndTbls$RNKAGE65 <- rank(x = -JndTbls$AGE65, na.last = "keep", ties.method = "max")
g <- JndTbls$RNKAGE17 <- rank(x = -JndTbls$AGE17, na.last = "keep", ties.method = "max")
h <- JndTbls$RNKDISABL <- rank(x = -JndTbls$DISABL, na.last = "keep", ties.method = "max")
i <- JndTbls$RNKSNGPNT <- rank(x = -JndTbls$SNGPNT, na.last = "keep", ties.method = "max")
j <- JndTbls$RNKMUNIT <- rank(x = -JndTbls$MUNIT, na.last = "keep", ties.method = "max")
k <- JndTbls$RNKMOBILE <- rank(x = -JndTbls$MOBILE, na.last = "keep", ties.method = "max")
l <- JndTbls$RNKCROWD <- rank(x = -JndTbls$CROWD, na.last = "keep", ties.method = "max")
m <- JndTbls$RNKNOVEH <- rank(x = -JndTbls$NOVEH, na.last = "keep", ties.method = "max")
n <- JndTbls$RNKGROUPQ <- rank(x = -JndTbls$GROUPQ, na.last = "keep", ties.method = "max")

#Sum The Ranks

JndTbls$SUMRANK = a+b+c+d+e+f+g+h+i+j+k+l+m+n

#Derive the Adaptive Capacity Index

JndTbls$ADPTVCAPACITY <- dplyr::percent_rank(JndTbls$SUMRANK)
```

> **This Finds How Much Each Variable Contributed to The Final Percent
> Rank (Optional)**

```{r}
# This Determines the Percentage Contribution to Final Rank
JndTbls$GEOID <- JndTbls$GEOID.x #Geoid.s was created in the previous join and needs to be renamed before joining it to the geometry
geoid <- which(colnames(JndTbls)=="GEOID")
a <- which(colnames(JndTbls)=="RNKPOV")
z <- which(colnames(JndTbls)=="RNKGROUPQ")
cols <- as.vector(names(JndTbls[a:z]))
Func <- function(x){round((abs(x)/abs(JndTbls$SUMRANK)),2)*100}
RnkPerc <- dplyr::mutate_at(.tbl = JndTbls, .vars = cols, .funs = Func)
RnkPerc <- RnkPerc[c(geoid, a:z)]
JndTbls <- dplyr::right_join(JndTbls, RnkPerc, by = "GEOID")
```

#### **Now to Bring in Geometry From Tigris**

```{r, results = "hide"}
JndTbls$GEOID <- JndTbls$GEOID.x #Geoid.x was created in the previous join and needs to be renamed before joining it to the geometry
options(tigris_use_cache = TRUE)
blockgroup_Geom <- tigris::block_groups(state = 'OR', county = Counties, cb = TRUE) #we are using simplified geometry here, this can be changed by setting cb = FALSE, but takes a little bit longer to download
JndTblsSP <- sp::merge(x = blockgroup_Geom, JndTbls, by = 'GEOID') #Now we're using the GEOID to join the Census Data to the Geometry
```

#### Now Let's Map Our Results!

```{r}
suppressPackageStartupMessages(require(leaflet))
suppressPackageStartupMessages(require(dplyr))
suppressPackageStartupMessages(require(leaflet.esri))

pop <- paste0(
  "<h3>","<b>", JndTblsSP$COUNTY.x,"</b>","</h3>",
  "<b>", JndTblsSP$CENSUS_TRACT.x, "</b>","<br>",
  "<b>","TOTAL POPULATION: ", prettyNum(JndTblsSP$TOTPOP, big.mark=","), " +/- ",JndTblsSP$B01003_001M,"</b>","<br>",
  "<b>","ADAPTIVE CAPACITY: ", round(100*(JndTblsSP$ADPTVCAPACITY), 1),"%","</b>","<br>",
  
  "<b><h4>SOCIOECONOMIC STATUS:<b></h4>",
  
  "<b>PCT LIVING IN POVERTY: </b>", round(100*(JndTblsSP$POV), 1), "%","<br>",
  "<b>PCT 16+ UNEMPLOYED: </b>", round(100*(JndTblsSP$UNEMP), 1), "%","<br>",
  "<b>PER CAPITA INCOME: </b>", "$", prettyNum(JndTblsSP$PCI, big.mark=","),"<br>",
  
  "<b><h4>LANGUAGE AND EDUCATION:<b></h4>",
  
  "<b>PCT OF POP 25+ LESS THAN 12th GRADE: </b>", round(100*(JndTblsSP$NOHSDP),1), "%","<br>",
  "<b>PCT NO ENGLISH: </b>", round(100*(JndTblsSP$LIMENG),1), "%","<br>",
  
  "<b><h4>DEMOGRAPHICS:</h4><b>",
  
  "<b>PCT UNDER AGE OF 17: </b>", round(100*(JndTblsSP$AGE17),1), "%","<br>",
  "<b>PCT 65+: </b>", round(100*(JndTblsSP$AGE65),1), "%","<br>",
  "<b>PCT DISABLED: </b>", round(100*(JndTblsSP$DISABL),1), "%","<br>",
  "<b>PCT CHLDRN LVNG IN SNGL PARENT HSHLDS: </b>", round(100*(JndTblsSP$SNGPNT),1), "%","<br>",
  
  "<b><h4>HOUSING AND TRANSPORTATION:</h4><b>",
  
  "<b>PCT LIVING IN MULTI-UNIT STRUCTURE: </b>", round(100*(JndTblsSP$MUNIT),1), "%","<br>",
  "<b>PCT MOBILE DWELLING: </b>", round(100*(JndTblsSP$MOBILE),1), "%","<br>",
  "<b>PCT LIVING IN CROWDED DWELLING: </b>", round(100*(JndTblsSP$CROWD),1), "%","<br>",
  "<b>PCT WITH NO VEHICLE ACCESS: </b>", round(100*(JndTblsSP$NOVEH),1), "%","<br>",
  "<b>PCT LIVING IN GROUP QUARTERS: </b>", round(100*(JndTblsSP$GROUPQ),1), "%","<br>",
  
  "<b><h4>RACIAL AND ETHNIC MAKEUP:<b></h4>",
  "<b>PCT MINORITY: </b>", round(100*(JndTblsSP$MINORITY),1), "%"
) #Here we're creating a popup for our interactive map, include whatever variables you want here!

BRBG <- RColorBrewer::brewer.pal(n = 11, name = "BrBG")

pal <- leaflet::colorQuantile(
  palette = BRBG,
  domain = JndTblsSP$ADPTVCAPACITY, n = 11, reverse = FALSE
) #Creating a Color Pallete, Feel free to choose whatever one you want, see the package Viridis for some cool options

myMap <- leaflet(data = JndTblsSP) %>% addTiles() %>% addPolygons(
    color = "#444444", 
    weight = 1, 
    smoothFactor = 0.5,
    opacity = 0.5, 
    fillOpacity = 0.5,
    fillColor = ~pal(ADPTVCAPACITY),
    highlightOptions = highlightOptions(color = "white", weight = 2, bringToFront = TRUE), 
    popup = pop, popupOptions = popupOptions(maxHeight = 250, maxWidth = 250, )) %>% addLegend("bottomright", 
    pal = pal, 
    values = JndTblsSP$ADPTVCAPACITY,
    title = "Adaptive Capacity Score",
    labFormat = labelFormat(prefix = ""),
    opacity = 0.75) 

myMap
```

#### **Use This Function to Export The Shapefile (or csv) to a Folder of Your Choice!**

**Export to Shapefile**

    rgdal::writeOGR(obj = JndTblsSP, dsn = paste0(Sys.getenv("HOME")), driver = "ESRI Shapefile", layer = "AdaptiveCapacityR", morphToESRI = FALSE)

**Or CSV**

The benefit of this is that it preserves the field names, I found it
better to export the geometry (blockgroup_Geom) alone and then join the
data by GEOID in ArcGIS.

    write.csv(x = JndTbls, paste0(Sys.getenv("HOME"), "\Adaptive_Capacity_R.csv"))

## Licensing/Disclaimer:

THIS SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS
OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF
MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.
IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY
CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE
SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.

THIS SOFTWARE HAS NOT BEEN PEER-REVIEWED AND IS SUBJECT TO REVISION. THE
AUTHOR NOR THE NATURE CONSERVANCY MAKE ANY WARRANTY AS TO THE CURRENCY,
COMPLETENESS, ACCURACY OR UTILITY OF THIS SOFTWARE. IT IS STRONGLY
RECOMMENDED THAT CAREFUL ATTENTION BE PAID TO THE DOCUMENTATION
ASSOCIATED WITH THIS SOFTWARE. THE AUTHOR NOR THE NATURE CONSERVANCY
SHALL BE HELD LIABLE FOR IMPROPER OR INCORRECT USE OF THIS SOFTWARE OR
ANY DATA PRODUCED HEREIN. ALL PARTIES UTILIZING THIS SOFTWARE MUST BE
INFORMED OF THESE RESTRICTIONS. THE NATURE CONSERVANCY AND AUTHOR SHALL
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