# Introduction

The multidimensional data model was defined with the aim of supporting data analysis. In multidimensional systems, data is structured in facts and dimensions1. The star model is widely accepted, it is recommended for use in widely distributed end-user tools.

The geographical dimension plays a fundamental role in multidimensional systems. It is very interesting to have the possibility of representing the reports obtained from multidimensional systems, using their geographic dimensions, on a map, or performing spatial analysis on them. This functionality is supported by package geomultistar.

The American Community Survey (ACS), within the United States Census Bureau (USCB), offers databases that can be structured using the multidimensional data model to take advantage of its characteristics to be consulted. A characteristic of these databases is that they have a geographic component in the form of a vector layer. For this reason, the structures offered by package geomultistar are suitable for representing this data.

The main objective of this package is to automatically generate multidimensional structures based on the geomultistar package from the geodatabases provided by the ACS, which can be easily queried by users.

Other packages are available that are very useful to access the same data, such as tidycensus, which works in an integrated way with tigris. The main characteristics of geogenr that distinguish it from other proposals are the following:

• it works locally, once available geodatabases are downloaded (can be downloaded using the package);

• supports access at the level of group of variables integrated in a layer, instead of at the level of variable or vector of variables;

• decomposes ACS composite variables into structured fields;

• allows to directly integrate variables of several years;

• and automatically structures the data using the multidimensional data model.

The rest of this document is structured as follows: First, the starting data are presented. Then, an illustrative example of how the package works is developed. Finally, the document ends with conclusions.

# American Community Survey 5-Year Estimates

The package is based on the geodatabases available on the TIGER/Line with Selected Demographic and Economic Data web page. For each year (as of 2010) a list of geodatabases appears under two sections:

• Legal and Administrative Areas;

• Statistical Areas.

As mentioned there for the year 2018 (the last one accessible at the moment), literally: “These geodatabases bring together geography from the 2018 TIGER/Line Shapefiles and data from the 2014-2018 American Community Survey (ACS) 5-year estimates.” Similar data are offered for the previous years for the periods corresponding to each one.

Each ACS geodatabase is structured in layers: a geographic layer, a metadata layer, and the rest are data layers. The data layers have a matrix form, the rows are indexed by instances of the geographic layer, the columns by variables defined in the metadata layer, the cells are numeric values. Here are two examples:

GEOID B00001e1 B00001m1 B00002e1 B00002m1

16000US0200065 60 -1 300 -1

16000US0200650 20 -1 20 -1

16000US0200760 350 -1 100 -1

16000US0200870 250 -1 60 -1

16000US0201090 200 -1 30 -1

16000US0201200 450 -1 100 -1

...

and

GEOID B01001e1 B01001m1 B01001e2 B01001m2 B01001e3 B01001m3 B01001e4 B01001m4 ...

16000US0100100 218 165 92 114 10 16 18 30

16000US0100124 2582 24 1313 98 45 37 14 19

16000US0100460 4374 24 1963 158 144 76 105 68

16000US0100484 641 159 326 89 10 17 16 11

16000US0100676 295 102 143 55 7 11 14 17

16000US0100820 32878 57 16236 453 1159 257 1151 209

...

Some of the defined variables are shown below.

Short Name Full Name

B00001e1 UNWEIGHTED SAMPLE COUNT OF THE POPULATION: Total: Total Population -- (Estimate)

B00001m1 UNWEIGHTED SAMPLE COUNT OF THE POPULATION: Total: Total Population -- (Margin of Error)

B00002e1 UNWEIGHTED SAMPLE HOUSING UNITS: Total: Housing Units -- (Estimate)

B00002m1 UNWEIGHTED SAMPLE HOUSING UNITS: Total: Housing Units -- (Margin of Error)

B01001e1 SEX BY AGE: Total: Total Population -- (Estimate)

B01001m1 SEX BY AGE: Total: Total Population -- (Margin of Error)

B01001e2 SEX BY AGE: Male: Total Population -- (Estimate)

B01001m2 SEX BY AGE: Male: Total Population -- (Margin of Error)

B01001e3 SEX BY AGE: Male: Under 5 years: Total Population -- (Estimate)

B01001m3 SEX BY AGE: Male: Under 5 years: Total Population -- (Margin of Error)

B01001e4 SEX BY AGE: Male: 5 to 9 years: Total Population -- (Estimate)

B01001m4 SEX BY AGE: Male: 5 to 9 years: Total Population -- (Margin of Error)

...

Each variable (Short Name) corresponds to combinations of various field values separated by a separator (:), forming a string (Full Name). The field name of each value is not available but the topics included are detailed on the web page Subjects Included in the Survey. There are thousands of variables of these characteristics (more than 50,000) that, in addition to the metadata layer, can be found on the TIGER/Line with Selected Demographic and Economic Data Record Layouts web page. For each combination of values, one variable associated with the estimate and another with the margin of error are defined. Within each layer, variables can be considered in groups, defined by the first part of the Full Name (for example UNWEIGHTED SAMPLE HOUSING UNITS and SEX BY AGE).

A module of geogenr package analyses the components of Full_name, structuring them in fields; and it allows access to variables in groups.

# An illustrative example

To obtain a geomultistar structure from the ACS data we can distinguish three phases:

• obtaining the data,

• data selection,

• and generation of results,

which are developed below.

Once the result structure is generated, we can define and execute queries on it.

## Obtaining the data

The data is available in the form of a geodatabase. One geodatabase for each area in each of the two area sections.

To consult the areas of each of the sections we use an object of class uscb_acs_5ye. When creating it, we can indicate a folder that will be used as the destination for downloads, if another is not indicated. Below are the operations to get the lists of available areas.

library(tidyr)
library(geogenr)

ua <- uscb_acs_5ye(folder = "../data/us/")

(laa <- ua %>% get_legal_and_administrative_areas())
#>  [1] "Alaska Native Regional Corporation"
#>  [2] "American Indian/Alaska Native/Native Hawaiian Area"
#>  [3] "Congressional District (116th Congress)"
#>  [4] "County"
#>  [5] "Elementary School District"
#>  [6] "Place"
#>  [7] "Secondary School District"
#>  [8] "State"
#>  [9] "State Legislative Districts - Lower Chamber"
#> [10] "State Legislative Districts - Upper Chamber"
#> [11] "Unified School District"
#> [12] "Zip Code Tabulation Area"

(sa <- ua %>% get_statistical_areas())
#>  [1] "Combined New England City and Town Area"
#>  [2] "Combined Statistical Area"
#>  [3] "Metropolitan Division"
#>  [4] "Metropolitan/Micropolitan Statistical Area"
#>  [5] "New England City and Town Area"
#>  [6] "New England City and Town Area Division"
#>  [7] "Public Use Microdata Area"
#>  [8] "Tribal Block Group"
#>  [9] "Tribal Census Tract"
#> [10] "Urban Area"

Since some geodatabases are included in the package, we have selected the area that has the smallest databases. Through the following operations2, we obtain the years for which the geodatabases of the chosen area are available on the web, and we download those corresponding to the selected years.

sa[6]
#>  [1] "New England City and Town Area Division"

(y <- ua %>% get_available_years_in_the_web(geodatabase = sa[6]))
#>  [1] 2013 2014 2015 2016 2017 2018

(y_res <- ua %>% download_geodatabases(geodatabase = sa[6], years = 2014:2015))
#>  [1] 2014 2015

## Data selection

Once we have the geodatabases available locally, we move on to selecting the data.

In this case we create a new object of class uscb_acs_5ye indicating the folder where the downloaded geodatabases are: the package data folder.

folder <- system.file("extdata", package = "geogenr")
folder <- stringr::str_replace_all(paste(folder, "/", ""), " ", "")
ua <- uscb_acs_5ye(folder = folder)

Using the following function, we check the years available locally for the selected area.

sa[6]
#> [1] "New England City and Town Area Division"

(y <- ua %>% get_available_years_downloaded(geodatabase = sa[6]))
#> [1] 2014 2015

Using the metadata included in the package (uscb_acs_metadata), the object of class uscb_acs_5ye where the data of the areas is included, the area of the selected geodatabase and a reference year, we create an object of class uscb_layer, from which we can consult the layer names available for those area and year, as shown below.

ul <- uscb_layer(uscb_acs_metadata, ua = ua, geodatabase = sa[6], year = 2015)
(layers <- ul %>% get_layer_names())
#> [1] "X00_COUNTS"      "X01_AGE_AND_SEX" "X02_RACE"

Of all the available layers, we obtain one from which we can consult the groups of variables that it includes, as shown in the following operations.

layers[3]
#> [1] "X02_RACE"

ul <- ul %>% get_layer(layers[3])
(layer_groups <- ul %>% get_layer_group_names())
#>  [1] "001 - RACE"
#>  [2] "003 - DETAILED RACE"
#>  [3] "008 - WHITE ALONE OR IN COMBINATION WITH ONE OR MORE OTHER RACES"
#>  [4] "009 - BLACK OR AFRICAN AMERICAN ALONE OR IN COMBINATION WITH ONE OR MORE OTHER RACES"
#>  [5] "010 - AMERICAN INDIAN AND ALASKA NATIVE ALONE OR IN COMBINATION WITH ONE OR MORE OTHER RACES"
#>  [6] "011 - ASIAN ALONE OR IN COMBINATION WITH ONE OR MORE OTHER RACES"
#>  [7] "012 - NATIVE HAWAIIAN AND OTHER PACIFIC ISLANDER ALONE OR IN COMBINATION WITH ONE OR MORE OTHER RACES"
#>  [8] "013 - SOME OTHER RACE ALONE OR IN COMBINATION WITH ONE OR MORE OTHER RACES"
#>  [9] "014 - AMERICAN INDIAN AND ALASKA NATIVE ALONE FOR SELECTED TRIBAL GROUPINGS"
#> [10] "015 - ASIAN ALONE BY SELECTED GROUPS"
#> [11] "016 - NATIVE HAWAIIAN AND OTHER PACIFIC ISLANDER ALONE BY SELECTED GROUPS"
#> [12] "017 - AMERICAN INDIAN AND ALASKA NATIVE (AIAN) ALONE OR IN ANY COMBINATION BY SELECTED TRIBAL GROUPINGS"
#> [13] "018 - ASIAN ALONE OR IN ANY COMBINATION BY SELECTED GROUPS"
#> [14] "019 - NATIVE HAWAIIAN AND OTHER PACIFIC ISLANDER ALONE OR IN ANY COMBINATION BY SELECTED GROUPS"

We obtain one of the groups by indicating its name, as shown below.

layer_groups[2]
#> [1] "003 - DETAILED RACE"

ul <- ul %>% get_layer_group(layer_groups[2])

Groups contain sets of variables. The variables of the selected group are shown below.

ul\$layer_group_columns
#>  [1] "GEOID"     "C02003e1"  "C02003m1"  "C02003e2"  "C02003m2"  "C02003e3"
#>  [7] "C02003m3"  "C02003e4"  "C02003m4"  "C02003e5"  "C02003m5"  "C02003e6"
#> [13] "C02003m6"  "C02003e7"  "C02003m7"  "C02003e8"  "C02003m8"  "C02003e9"
#> [19] "C02003m9"  "C02003e10" "C02003m10" "C02003e11" "C02003m11" "C02003e12"
#> [25] "C02003m12" "C02003e13" "C02003m13" "C02003e14" "C02003m14" "C02003e15"
#> [31] "C02003m15" "C02003e16" "C02003m16" "C02003e17" "C02003m17" "C02003e18"
#> [37] "C02003m18" "C02003e19" "C02003m19"

## Generation of results

Once we have obtained a group of variables, we can obtain the associated data in various formats.

It offers the possibility of obtaining it as a tibble, as shown below (the table is not shown due to the high number of columns it has).

ft <- ul %>% get_flat_table(remove_geometry = FALSE)

names(ft)
#>  [1] "year"                              "cnectafp"
#>  [3] "nectafp"                           "nctadvfp"
#>  [5] "geoid"                             "name"
#>  [9] "mtfcc"                             "aland"
#> [11] "awater"                            "intptlat"
#> [13] "intptlon"                          "shape_length"
#> [15] "shape_area"                        "geoid_data"
#> [17] "short_name"                        "full_name"
#> [19] "inf_code"                          "group_code"
#> [21] "spec_code"                         "inf"
#> [23] "group"                             "demographic_race"
#> [25] "demographic_race_spec"             "demographic_total_population"
#> [27] "demographic_total_population_spec" "estimate"
#> [29] "margin_of_error"                   "shape"

nrow(ft)
#> [1] 190

We can also get an object of the geomultistar class.

gms <- ul %>% get_geomultistar()

The first rows of the dimension and fact tables are shown below.

when_key year
1 2015
where_key cnectafp nectafp nctadvfp geoid name namelsad lsad mtfcc aland awater intptlat intptlon shape_length shape_area geoid_data
1 715 71650 71654 7165071654 Boston-Cambridge-Newton, MA Boston-Cambridge-Newton, MA NECTA Division M7 G3220 3.668e+09 7.13e+08 +42.2933266 -071.0181929 7.653 0.4783 35500US7165071654
2 715 71650 72104 7165072104 Brockton-Bridgewater-Easton, MA Brockton-Bridgewater-Easton, MA NECTA Division M7 G3220 352799175 8831197 +42.0216172 -071.0267170 1.077 0.03932 35500US7165072104
3 715 71650 73104 7165073104 Framingham, MA Framingham, MA NECTA Division M7 G3220 532516314 24039093 +42.2761738 -071.4822008 1.738 0.06073 35500US7165073104
4 715 71650 73604 7165073604 Haverhill-Newburyport-Amesbury Town, MA-NH Haverhill-Newburyport-Amesbury Town, MA-NH NECTA Division M7 G3220 702086333 40447613 +42.8671722 -071.0254982 1.416 0.08179 35500US7165073604
5 715 71650 74204 7165074204 Lawrence-Methuen Town-Salem, MA-NH Lawrence-Methuen Town-Salem, MA-NH NECTA Division M7 G3220 207735751 9917120 +42.7282758 -071.1630701 0.9094 0.02392 35500US7165074204
6 715 71650 74804 7165074804 Lowell-Billerica-Chelmsford, MA-NH Lowell-Billerica-Chelmsford, MA-NH NECTA Division M7 G3220 863143106 27403003 +42.6141693 -071.4837821 2.441 0.09771 35500US7165074804
what_key short_name full_name inf_code group_code spec_code inf group demographic_race demographic_race_spec demographic_total_population demographic_total_population_spec
1 C02003_01 DETAILED RACE: Total: Total Population C02 003 1 RACE DETAILED RACE Total Total Population
2 C02003_02 DETAILED RACE: Population of one race: Total Population C02 003 2 RACE DETAILED RACE Population of one race Total Population
3 C02003_03 DETAILED RACE: Population of one race: White: Total Population C02 003 3 RACE DETAILED RACE Population of one race White Total Population
4 C02003_04 DETAILED RACE: Population of one race: Black or African American: Total Population C02 003 4 RACE DETAILED RACE Population of one race Black or African American Total Population
5 C02003_05 DETAILED RACE: Population of one race: American Indian and Alaska Native: Total Population C02 003 5 RACE DETAILED RACE Population of one race American Indian and Alaska Native Total Population
6 C02003_06 DETAILED RACE: Population of one race: Asian alone: Total Population C02 003 6 RACE DETAILED RACE Population of one race Asian alone Total Population
when_key where_key what_key estimate margin_of_error nrow_agg
1 1 1 2846699 131 1
1 1 2 2754158 3195 1
1 1 3 2134196 4429 1
1 1 4 274896 2907 1
1 1 5 4817 603 1
1 1 6 253518 2264 1

Once we have verified that the data for the reference year is what we need, we can expand our database considering the rest of the years available in the folder. The only requirement to consider a year is that its variable structure be the same as that of the reference year.

To do this, we create a class uscb_folder object from the reference year object, as shown below.

uf <- uscb_folder(ul)

We can get a tibble from the new object. In this case it has more rows. In this case you have more rows than for just one year, as you would expect.

cft <- uf %>% get_common_flat_table()

nrow(cft)
#> [1] 380

We can also get a geomultistar object.

cgms <- uf %>% get_common_geomultistar()

Instead of displaying all the tables, we focus on the table in the when dimension.

when_key year
1 2014
2 2015

Includes data for all available years.

## Queries with geographic information

Once we have a geomultistar object, we can use the functionality of the geomultistar package.

Specifically, the only field that has geographic information directly associated with is geoid. If we want to associate that information to other fields of the dimension, such as the name field, we must do it using the operation shown below.

library(geomultistar)

cgms <- cgms  %>%
define_geoattribute(
attribute = c("name"),
from_attribute = "geoid"
)

We can define multidimensional queries using the functionality of the starschemar package, as shown below.

library(starschemar)

gdqr <- dimensional_query(cgms) %>%
select_dimension(name = "where",
attributes = c("name")) %>%
select_dimension(name = "what",
attributes = c("short_name", "demographic_race_spec")) %>%
select_fact(name = "detailed_race",
measures = c("estimate")) %>%
filter_dimension(name = "when", year == "2015") %>%
filter_dimension(name = "what", demographic_race_spec == "Asian alone") %>%
run_geoquery()

The first rows of the result can be seen below in table form.

short_name demographic_race_spec name estimate nrow_agg Shape
C02003_06 Asian alone Boston-Cambridge-Newton, MA 253518 1 MULTIPOLYGON (((-71.39 42.3…
C02003_06 Asian alone Brockton-Bridgewater-Easton, MA 3548 1 MULTIPOLYGON (((-71.08 42.1…
C02003_06 Asian alone Framingham, MA 17611 1 MULTIPOLYGON (((-71.39 42.3…
C02003_06 Asian alone Haverhill-Newburyport-Amesbury Town, MA-NH 2375 1 MULTIPOLYGON (((-71.22 42.9…
C02003_06 Asian alone Lawrence-Methuen Town-Salem, MA-NH 6752 1 MULTIPOLYGON (((-71.19 42.7…
C02003_06 Asian alone Lowell-Billerica-Chelmsford, MA-NH 36648 1 MULTIPOLYGON (((-71.66 42.6…
C02003_06 Asian alone Lynn-Saugus-Marblehead, MA 8476 1 MULTIPOLYGON (((-70.9 42.48…
C02003_06 Asian alone Nashua, NH-MA 10322 1 MULTIPOLYGON (((-71.88 42.8…
C02003_06 Asian alone Peabody-Salem-Beverly, MA 3381 1 MULTIPOLYGON (((-71 42.56, …
C02003_06 Asian alone Taunton-Middleborough-Norton, MA 2125 1 MULTIPOLYGON (((-71.19 41.9…

The result is a vector layer that we can save, perform spatial analysis or queries on it, or we can see it as a map, using the functions associated with the sf class.

class(gdqr)
#> [1] "sf"         "tbl_df"     "tbl"        "data.frame"

plot(gdqr[,"estimate"])

# Conclusions

The American Community Survey (ACS) offers geodatabases with geographic information and associated data of interest to researchers in the area. These data can be accessed through various alternatives in which you must indicate the year and variable names. Due to the large number of variables and their structure, this operation is not easy.

The geogenr package offers an alternative that allows you to download the geodatabases that are considered necessary and access the variables by selecting data layers and logical groups of variables. Additionally, it automatically generates a multidimensional structure that includes the available geographic information. From this structure, multidimensional queries that include the available geographic information can easily be defined.

The data obtained can be processed with the sf package to define spatial queries or analysis, be presented in maps or saved as a file to be used by a GIS (Geographical Information System).

1. Basic concepts of dimensional modelling and star schemas are presented in starschemar vignettes.↩︎

2. These operations are not running in the vignette. The result of executing them locally to obtain the geodatabases included in the package is displayed.↩︎