Victoria 2 States Map

These county (or county equivalent)-based reference maps show and label the census tracts as delineated to support 2020 Census data dissemination. These maps also show and label federal and state American Indian reservations, Alaska Native Regional Corporations, states (or state equivalents), counties, county subdivisions, consolidated cities, and places. Additionally, these maps display a base feature network including roads, railroads, and water bodies. These features are labeled as scale permits. The map sheet configuration is optimized to keep the number of map sheets to a minimum. Each county will be covered by one or more parent map sheets at a single scale. Inset map sheets at larger scales are created where there are clusters of census tracts that cannot be identified at the parent map scale. An index map showing the sheet configuration is created for all counties requiring more than one parent map sheet. The map sheet size is 36 by 42 inches.

LTCFs (e.g., nursing homes/skilled nursing, long-term care for the developmentally disabled, and assisted living facilities) from all 50 states and U.S. territories report data on influenza virus infections among residents through the National Healthcare Safety Network (NHSN) Long-term Care Facility Component. During week 4, 164 (1.1%) of 14,384 reporting facilities reported at least one influenza positive test among their residents. This decreased by > 5% compared to week 3.

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The Influenza Hospitalization Surveillance Network (FluSurv-NET) conducts population-based surveillance for laboratory-confirmed influenza-related hospitalizations in select counties in 13 states and represents approximately 9% of the U.S. population. FluSurv-NET hospitalization data are preliminary. As data are received each week, prior case counts and rates are updated accordingly.

Since states can be divided into multiple districts, we have to use a custom shape map for districts. By importing the TopoJSON map of the U.S. judicial districts into Power BI Desktop, we can then visualize the annual fiscal district attorney data. The image below uses a custom map named 2000_districts_extra_simplified_CONUS.json file.

* This is one of the 29 OSHA-approved State Plans. Twenty-two State Plans (21 states and Puerto Rico) cover both private and state and local government workplaces. The other seven State Plans (Connecticut, Illinois, Maine, Massachusetts, New Jersey, New York, and the Virgin Islands) cover state and local government workers only.

To introduce these functions, we rely on the ozmaps package by Michael Sumner which provides maps for Australian state boundaries, local government areas, electoral boundaries, and so on.21 To illustrate what an sf data set looks like, we import a data set depicting the borders of Australian states and territories:

This output shows some of the metadata associated with the data (discussed momentarily), and tells us that the data is essentially a tibble with 9 rows and 2 columns. One advantage to sf data is immediately apparent, we can easily see the overall structure of the data: Australia is comprised of six states and some territories. There are 9 distinct geographical units, so there are 9 rows in this tibble (cf. mi_counties data where there is one row per polygon vertex).

The most important column is geometry, which specifies the spatial geometry for each of the states and territories. Each element in the geometry column is a multipolygon object which, as the name suggests, contains data specifying the vertices of one or more polygons that demark the border of a region. Given data in this format, we can use geom_sf() and coord_sf() to draw a serviceable map without specifying any parameters or even explicitly declaring any aesthetics:

It is worth noting that the first layer to this plot maps the fill aesthetic in onto a variable in the data. In this instance the NAME variable is a categorical variable and does not convey any additional information, but the same approach can be used to visualise other kinds of area metadata. For example, if oz_states had an additional column specifying the unemployment level in each state, we could map the fill aesthetic to that variable.

Adding labels to maps is an example of annotating plots (Chapter 8) and is supported by geom_sf_label() and geom_sf_text(). For example, while an Australian audience might be reasonably expected to know the names of the Australian states (and are left unlabelled in the plot above) few Australians would know the names of different electorates in the Sydney metropolitan region. In order to draw an electoral map of Sydney, then, we would first need to extract themap data for the relevant electorates, and then add the label. The plot below zooms in on the Sydney region by specifying xlim and ylim in coord_sf() and then uses geom_sf_label() to overlay each electorate with a label:

One limitation to displaying only the raw image is that it is not easy to work out where the relevant landmasses are, and we may wish to overlay the satellite data with the oz_states vector map to show the outlines of Australian political entities. However, some care is required in doing so because the two data sources are associated with different coordinate reference systems. To project the oz_states data correctly, the data should be transformed using the st_transform() function from the sf package. In the code below, we extract the CRS from the sat_vis raster object, and transform the oz_states data to use the same system.

The rnaturalearth package29 bundles up the free, high-quality data from It contains country borders, and borders for the top-level region within each country (e.g. states in the USA, regions in France, counties in the UK).

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