surv-cens >> ts anal exs

Author: ercbk

_book/qmd/_resources/Surveys,_Census_Data.resources/map-wkflw-ex1-1.png

@@ -4131,7 +4131,7 @@ <h3 class="unnumbered anchored" data-anchor-id="inhomogeneity">Inhomogeneity</h3
 <li>Otherwise, <span class="math inline">\(\lambda(u)\)</span> will be estimated from the data by a leave-one-out kernel smoother. Manually this would be <code>density(swp, sigma = bw.diggle, at="points")</code>.</li>
 </ul></li>
 <li><p>On p.&nbsp;689 in the <span style="color: #990000">{spatstat}</span> book, a permutation test procedure is performed to test whether a point pattern is correlation-stationary (inhomogeneous) or locally scaled. Testing for whether the pattern is homogeneous or locally scaled and inhomogeneous, etc. could also have been done as well I think.</p></li>
-<li><p><span class="ribbon-highlight">Example</span>: <span style="color: #990000">{spatstat}</span> book</p>
+<li><p><span class="ribbon-highlight">Example</span>: <span style="color: #990000">{spatstat}</span> book, pp.&nbsp;244-246</p>
 <div class="tabset-margin-container"></div><div class="panel-tabset">
 <ul class="nav nav-tabs" role="tablist"><li class="nav-item" role="presentation"><a class="nav-link active" id="tabset-5-1-tab" data-bs-toggle="tab" data-bs-target="#tabset-5-1" role="tab" aria-controls="tabset-5-1" aria-selected="true">Data</a></li><li class="nav-item" role="presentation"><a class="nav-link" id="tabset-5-2-tab" data-bs-toggle="tab" data-bs-target="#tabset-5-2" role="tab" aria-controls="tabset-5-2" aria-selected="false">Homogeneous K and L</a></li><li class="nav-item" role="presentation"><a class="nav-link" id="tabset-5-3-tab" data-bs-toggle="tab" data-bs-target="#tabset-5-3" role="tab" aria-controls="tabset-5-3" aria-selected="false">Inhomogeneous K and L</a></li><li class="nav-item" role="presentation"><a class="nav-link" id="tabset-5-4-tab" data-bs-toggle="tab" data-bs-target="#tabset-5-4" role="tab" aria-controls="tabset-5-4" aria-selected="false">Global Envelope</a></li></ul>
 <div class="tab-content">

_book/qmd/_resources/Surveys,_Census_Data.resources/tidyc-migfl-ex1-1.png

@@ -1732,7 +1732,7 @@ fig-align: left
 
 -   On p. 689 in the [{spatstat}]{style="color: #990000"} book, a permutation test procedure is performed to test whether a point pattern is correlation-stationary (inhomogeneous) or locally scaled. Testing for whether the pattern is homogeneous or locally scaled and inhomogeneous, etc. could also have been done as well I think.
 
--   [Example]{.ribbon-highlight}: [{spatstat}]{style="color: #990000"} book
+-   [Example]{.ribbon-highlight}: [{spatstat}]{style="color: #990000"} book, pp. 244-246
 
     :::::: panel-tabset
     ## Data

_book/qmd/geospatial-point-patterns.html

@@ -557,6 +557,7 @@ title: "Scrapsheet"
             ``` r
             library(tidycensus)
             library(tidyverse)
+            library(mapview)
 
             top100counties <- get_acs(
               geography = "county",
@@ -599,15 +600,14 @@ title: "Scrapsheet"
         -   Make 100 Maps
 
             ``` r
-            library(mapview)
-
-            wfh_maps <- map(wfh_tract_list, function(county) {
-              mapview(
-                county, 
-                zcol = "estimate",
-                layer.name = "% working from home"
-              ) 
-            })
+            wfh_maps <- 
+              map(wfh_tract_list, function(county) {
+                mapview(
+                  county, 
+                  zcol = "estimate",
+                  layer.name = "% working from home"
+                ) 
+              })
             ```
 
 -   Small Area Time Series Analysis (2:40)
@@ -640,23 +640,35 @@ title: "Scrapsheet"
 
                 -   get more details
 
-            -   Aerial Interpolation (see book for more details)
+            -   Aerial Interpolation (see [book ](https://walker-data.com/census-r/spatial-analysis-with-us-census-data.html?q=small#small-area-time-series-analysis)for more details)
 
                 -   Check for incongruent boundaries
 
                     ``` r
                     library(sf)
 
-                    wfh_17 <- get_acs(geography = "tract", variables = "B08006_017", year = 2017,
-                                      state = "UT", county = "Salt Lake", geometry = TRUE) %>%
+                    wfh_17 <- 
+                      get_acs(geography = "tract", 
+                              variables = "B08006_017", 
+                              year = 2017,
+                              state = "UT", 
+                              county = "Salt Lake", 
+                              geometry = TRUE) |> 
                       st_transform(6620)
 
-                    wfh_22 <- get_acs(geography = "tract", variables = "B08006_017", year = 2022,
-                                      state = "UT", county = "Salt Lake", geometry = TRUE) %>%
+                    wfh_22 <- 
+                      get_acs(geography = "tract", 
+                              variables = "B08006_017", 
+                              year = 2022,
+                              state = "UT", 
+                              county = "Salt Lake", 
+                              geometry = TRUE) |> 
                       st_transform(6620)
                     ```
 
-                    -   get details on crs projection
+                    -   Process is quicker on a projected coordinated system
+
+                        -   [EPSG:6620](https://epsg.io/6620) is NAD83(2011) / Utah North
 
                     -   get details on how he found incongruent boundaries
 
@@ -665,8 +677,8 @@ title: "Scrapsheet"
                     ``` r
                     library(sf)
 
-                    wfh_22_to_17 <- wfh_22 %>%
-                      select(estimate) %>%
+                    wfh_22_to_17 <- wfh_22 |> 
+                      select(estimate) |> 
                       st_interpolate_aw(to = wfh_17, extensive = TRUE)
                     ```