Scatterplot r studio
This allows for comparability between areas with different numbers of neighbors. For example, if census tract 1 has 3 neighbors, each of the neighbors will have a weight of 1/3. Here, style = "W" indicates that the weights for each spatial unit are standardized to sum to 1 (this is known as row standardization - see Handout 6). In the command, you first put in your neighbor nb object ( sacb) and then define the weights style = "W". sacw<-nb2listw(sacb, style="W", zero.policy = TRUE) You will need to employ the nb2listw() command from the spdep package, which will you give you a spatial weights object. The weight determines how much each neighbor counts. The next step is to assign weights to each neighbor relationship. We’ve established who our neighbors are by creating an nb object using poly2nb(). The argument remove_slivers = TRUE removes these slivers. But, the other three are clipped - the portions that are within the boundary are kept (in blue), and the rest (with hash marks) are discarded from the map.īecause spatial data are not always precise, when you clip you’ll sometimes get unwanted sliver polygons. With a clip, one tract is not clipped because it falls completely within the city (the top left tract). To be clear what a clip is doing, the Figure below shows a clip of the city example shown in the first Figure above. The argument target = specifies the dough and clip = specifies the cookie cutter. Map the clipped tracts and the city boundaries. <- ms_clip(target = ca.tracts, clip = sac.city, remove_slivers = TRUE)
#Scatterplot r studio code
In the code below, target = ca.tracts tells R to cut out ca.tracts using the sac.city boundaries. We use the function ms_clip() which is in the rmapshaper package. Clipping will keep just the portion of the tract inside the city boundary and discards the rest of the tract. One way of dealing with this is to keep or clip the portion of the tract that is inside the boundary. Tracts falling in (County) and out (City) of boundaries In contrast, the right diagram is an example of a city on top of four tracts - one tract falls neatly inside (top left), but the other three spill out. The left diagram in the Figure below is an example of a county in red and four tracts in black - all the tracts fall neatly into the county boundary. Recall from Handout 3 that census tracts neatly fall within a county’s boundary (remember the census geography hierarchy diagram from Handout 3).
Think of what were doing here as something similar to taking a cookie cutter shaped like the Sacramento city (in our case, the sf object sac.city) and cutting out the city from our cookie dough of census tracts ( ca.tracts). In our case, we want to keep California tracts that are in Sacramento city. A common spatial data wrangling task is to subset a set of spatial objects based on their location relative to another spatial object. Spatial Data Wrangling involves cleaning or altering your data set based on the geographic location of features. However, not just any old data wrangling, but spatial data wrangling. In order to extract the Sacramento tracts, we need to do some data wrangling. While there is a county = argument in get_acs(), there is no place = argument. This includes the GEOID, which only provides state and county census IDs. Looking at the variables in the data frame ca.tracts, we find that there is no variable that indicates whether the tract belongs to Sacramento city. Before we can do this, we need to keep the tracts from ca.tracts that are in Sacramento City sac.city. The goal in this lab is to compute the spatial autocorrelation of percent foreign born in Sacramento City.