feat: Minor revisions to content.

docs/flight-delays.md

@@ -35,6 +35,8 @@ vg.vconcat(
     vg.intervalX({ as: $brush }),
     vg.xDomain(vg.Fixed),
     vg.yTickFormat("s"),
+    vg.xLabel("Delay (minutes)"),
+    vg.yLabel("Number of Flights"),
     vg.width(600),
     vg.height(150)
   ),
@@ -46,6 +48,8 @@ vg.vconcat(
     vg.intervalX({ as: $brush }),
     vg.xDomain(vg.Fixed),
     vg.yTickFormat("s"),
+    vg.xLabel("Time (hour of day)"),
+    vg.yLabel("Number of Flights"),
     vg.width(600),
     vg.height(150)
   ),
@@ -57,6 +61,8 @@ vg.vconcat(
     vg.intervalX({ as: $brush }),
     vg.xDomain(vg.Fixed),
     vg.yTickFormat("s"),
+    vg.xLabel("Distance (miles)"),
+    vg.yLabel("Number of Flights"),
     vg.width(600),
     vg.height(150)
   )
@@ -98,6 +104,7 @@ vg.vconcat(
       vg.xAxis("top"),
       vg.yAxis(null),
       vg.xLabelAnchor("center"),
+      vg.xLabel("Time (hour of day)"),
       vg.width(605),
       vg.height(70)
     ),
@@ -131,6 +138,7 @@ vg.vconcat(
       vg.xAxis(null),
       vg.yAxis("right"),
       vg.yLabelAnchor("center"),
+      vg.yLabel("Delay (minutes)"),
       vg.width(80),
       vg.height(455)
     )
@@ -161,6 +169,8 @@ vg.hconcat(
     vg.colorScheme("blues"),
     vg.colorScale("symlog"),
     vg.xZero(true),
+    vg.xLabel("Time (hour of day)"),
+    vg.yLabel("Delay (minutes)"),
     vg.xyDomain(vg.Fixed),
     vg.width(315),
     vg.height(300)
@@ -174,6 +184,9 @@ vg.hconcat(
     vg.intervalX({ as: $filter, brush: {fill: "none", stroke: "#888"} }),
     vg.colorScheme("blues"),
     vg.colorScale("symlog"),
+    vg.xScale("log"),
+    vg.xLabel("Distance (miles, log scale)"),
+    vg.yLabel("Delay (minutes)"),
     vg.xyDomain(vg.Fixed),
     vg.width(315),
     vg.height(300)

docs/nyc-taxi-rides.md

@@ -11,7 +11,8 @@ const vg = vgplot(vg => [ vg.loadParquet("trips", url(trips)) ]);
 # NYC Taxi Rides
 ## Pickup and dropoff points for 1M NYC taxi rides on Jan 1-3, 2010.
 
-In the data loader, we ingest a remote file into DuckDB and project [_longitude_, _latitude_] coordinates (in the database!) to spatial positions with units of 12 inch feet.
+In a data loader, we ingest a remote file into DuckDB and project [_longitude_, _latitude_] coordinates (in the database!) to spatial positions with units of 12 inch feet.
+We then load the prepared data to visualize taxi pickup and dropoff locations, as well as the volume of rides by the time of day.
 
 _Please wait a few seconds for the dataset to load._
 
@@ -83,11 +84,12 @@ vg.plot(
   ),
   vg.intervalX({ as: $filter }),
   vg.yTickFormat('s'),
-  vg.xLabel('Pickup Hour →'),
+  vg.xLabel('Pickup Hour'),
+  vg.yLabel('Number of Rides'),
   vg.width(680),
   vg.height(100)
 )
 ```
 
-Select an interval in a plot to filter the maps.
-What spatial patterns can you find?
+_Select an interval in a plot to filter the maps.
+What spatial patterns can you find?_

docs/observable-latency.md

@@ -11,19 +11,18 @@ const vg = vgplot(vg => [ vg.loadParquet("latency", url(latency)) ]);
 # Observable Web Latency
 ## Recreating a custom graphic using Mosaic vgplot
 
-The Observable Framework documentation includes a wonderful example about [Analyzing web logs](https://observablehq.com/framework/examples/api/), which looks at the latency (response time) of various routes on the Observable.com site.
-
+The Observable Framework documentation includes a wonderful example about [analyzing web logs](https://observablehq.com/framework/examples/api/), visualizing the latency (response time) of various routes on the Observable.com site.
 The marquee graphic is a pixel-level heatmap of over 7 million requests to Observable servers over the course of a week.
 The chart plots time vs. latency, where each pixel is colored according to the most common route (URL pattern) in that time and latency bin.
 
 That said, a lot is going on in the original [custom heatmap component](https://github.com/observablehq/framework/blob/main/examples/api/docs/components/apiHeatmap.js):
 
 - The data is pre-binned and aggregated for fast loading
 - Observable Plot and HTML Canvas code are intermixed in non-trivial ways
-- Frame-based animation is used to progressively render the graphic
+- Frame-based animation is used to progressively render the graphic, presumably to combat sluggish rendering
 
-Below we re-implement this graphic using [Mosaic vgplot](https://uwdata.github.io/mosaic/what-is-mosaic/), using a simple, standalone specification.
-We also leverage Mosaic's facilities for scalable filtering and cross-chart linking.
+Here we re-create this graphic with [Mosaic vgplot](https://uwdata.github.io/mosaic/what-is-mosaic/), using a standalone specification.
+We also leverage Mosaic's support for cross-chart linking and scalable filtering.
 
 ```js
 const $filter = vg.Selection.intersect();

observablehq.config.ts

@@ -29,7 +29,7 @@ export default {
   // header: "", // what to show in the header (HTML)
   footer: `<a href="https://idl.cs.washington.edu/">Interactive Data Lab, University of Washington</a>`,
   toc: true, // whether to show the table of contents
-  pager: false, // whether to show previous & next links in the footer
+  pager: true, // whether to show previous & next links in the footer
   // root: "docs", // path to the source root for preview
   // output: "dist", // path to the output root for build
 };