test update

content/page/about.mdx

@@ -1,7 +1,7 @@
 ---
 sections:
   - title: Project Motivation
-    body: "\_More coming soon.\n"
+    body: "\_More coming soon. More is now. \n"
   - title: Data
     body: >
       #### Methodology
@@ -35,7 +35,7 @@ sections:
          \
          We weighted each location by its sales volume - in the case of Dollar Stores, Supercenters, and Warehouse Stores, we divide sales based on estimates of the percentage of sales that area food items. To compare values over time, we adjust for inflation (using CPI) and adjust for median income and goods pricing (where higher sales volumes in affluent areas may represent fewer total groceries sold, and the opposite may be true in lower income areas).
       2. Define demand locations: we use census data crosswalked to 2020 census
-      tract geographies to estimate the number of people in a given area.. 
+      tract geographies to estimate the number of people in a given area..
 
       3. Define travel time: we use the straight line distance between census
       block groups, aggregated to census tracts, to estimate the travel time
@@ -47,8 +47,7 @@ sections:
       distance or time threshold that people are willing to travel to access a
       grocery store.\
          \
-         A distance decay function is applied, which assumes that the attractiveness or utility of a grocery store decreases as the distance from the store increases. Our weighting is linear, (α=1) which means that a store would have to be twice as attractive for someone to travel twice as far. We use a distance threshold of 1.2km (β=1200) to estimate the threshold at which distance sensitivity starts to decay more rapidly. \
-
+         A distance decay function is applied, which assumes that the attractiveness or utility of a grocery store decreases as the distance from the store increases. Our weighting is linear, (α=1) which means that a store would have to be twice as attractive for someone to travel twice as far. We use a distance threshold of 1.2km (β=1200) to estimate the threshold at which distance sensitivity starts to decay more rapidly. \\
       5. Calculate accessibility:  for each census tract, we calculate the
       accessibility score to grocery stores. We sum weighted supply values of
       all grocery stores within the catchment area, modified by the distance and
@@ -58,20 +57,19 @@ sections:
       resources, which is very important for services that can hit capacity
       limits such as Healthcare, but in the case of grocery supply it is very
       rare in the US context that a store would be fully sold out of viable food
-      supply. \
-
+      supply. \\
 
       6. Normalize and interpret: for the food access score, we assign a
       percentile to each tract's accessibility score from 0 to 100 relative to
       all tracts. For counties and states, we calculate the population weighted
       average of accessibility scores for all the tracts within, and then assign
-      a percentile relative to all counties or states. 
+      a percentile relative to all counties or states.
 
 
       Market concentration: To estimate market concentration we use the
       Herfindahl-Hirschman Index (HHI), a widely used measure of market
       concentration. HHI is particularly useful when assessing the competitive
-      landscape of industries like grocery stores. 
+      landscape of industries like grocery stores.
 
 
       To calculate HHI, we use the following steps:
@@ -89,22 +87,22 @@ sections:
       equates to a reasonable walking distance when traffic and street grids are
       considered.\
          \
-         We assign a driving time of 5 to 20 minutes based on the density of a given census tract and its neighbors (spatial lagged value) to differentiate tracts that area next to urban areas but are less dense, and truly rural or remote areas. We take the density values and normalize them from 0 to 100, exponentially scale the values to emphasize lower driving tolerances, and normalized again. Based on these scores, we create driving service areas using the Microsoft Bing isochrone API. The estimate the service area based on modeled traffic at 6pm on a Saturday evening in July. We apply a 500 foot linear buffer to the isochrones to capture strip malls or other locations that are just outside the calculated area. 
+         We assign a driving time of 5 to 20 minutes based on the density of a given census tract and its neighbors (spatial lagged value) to differentiate tracts that area next to urban areas but are less dense, and truly rural or remote areas. We take the density values and normalize them from 0 to 100, exponentially scale the values to emphasize lower driving tolerances, and normalized again. Based on these scores, we create driving service areas using the Microsoft Bing isochrone API. The estimate the service area based on modeled traffic at 6pm on a Saturday evening in July. We apply a 500 foot linear buffer to the isochrones to capture strip malls or other locations that are just outside the calculated area.
       2. Find stores within a census tract's service area: based on the service
       area of a tract, we find all the stores nearby based on their location. \
          \
-         For service areas that have no locations, we increase the threshold by 10 minutes (eg. 20 to 30, 30 to 40) up to a 60 minute driving tolerance until a store or stores are in the area. 
+         For service areas that have no locations, we increase the threshold by 10 minutes (eg. 20 to 30, 30 to 40) up to a 60 minute driving tolerance until a store or stores are in the area.
       3. Find the ultimate parent chain of the stores: for each store in the
       service area, we identify its parent chain based on the 'Parent Number'
       column of the Reference USA data. This links an individual grocery chain
-      to their parent company (eg. Harris Teeter is owned by Kroger). 
+      to their parent company (eg. Harris Teeter is owned by Kroger).
 
       4. Calculate the HHI index: based on the total sales of each parent chain
       in the service area of a tract, we calculate HHI. In essence, this measure
       reflects how dominant stores are in the area, where a value of 1
       represents total dominance (1 store has all of the sales) and a value
       closer to zero reflects a more dispersed market (0.5 means two stores have
-      equal sales, 0.1 means ten stores, and so on). 
+      equal sales, 0.1 means ten stores, and so on).
 
       5. Normalize and interpret: We take the HHI values for each tract and
       assign a percentile value from 0 to 100 relative to all tracts. We invert