update description of compound transport var for mpadge/UrbanAnalyst#38

src/example.md

@@ -39,7 +39,7 @@ city (each measured on its own distinct scale).
 | Times (rel) | 1.09 | 1.03 |
 | Num. Transfers | 0.9 | 1.5 |
 | Intervals (min) | 6.9 | 4.9 |
-| Transport | 30.1 | 36.6 |
+| Transport | 33.2 | 25.5 |
 | Pop. Dens. | 3 | 3 |
 | School Dist (m) | 338 | 186 |
 | Bike Index | 0.81 | 0.76 |
@@ -91,10 +91,9 @@ minutes) until the next equivalent service. Intervals in Paris are slightly
 under 5 minutes, whereas values in Berlin are just under 7 minutes.
 
 Finally, the "Compound Transport" variable simply multiplies absolute travel
-times by numbers of transfers by intervals. Low values of this statistic
-reflect fast and frequent transport with few transfers. Because of the
-relatively high numbers of transfers necessary in Paris, it has a considerably
-higher value of this statistic than Berlin.
+times by intervals between services. Low values of this statistic reflect fast
+and frequent transport. This statistic also indicates considerably superior
+service in Paris compared with Berlin.
 
 ### Other Variables
 

src/variables.md

@@ -144,17 +144,17 @@ times for the next equivalent journey out to that distance.
 
 All three of the statistics described above - travel times, intervals, and
 numbers of transfers - are measured such that lower values are more desirable.
-All three are then directly multiplied to generate a "*compound travel
+Travel times are then directly multiplied by (a logarithmically-transformed
+version of) intervals between services to generate a "*compound travel
 statistic*". Low values of this statistic only arise in locations which have
-fast travel times, short intervals between services, and few transfers. Low
-values may accordingly always be interpreted as indicating overall good
-transport services. In contrast, high values may arise through various
-combinations of variables, from extremely high values of one single variable,
-to less extreme combinations of two or three of the variables. It is thus
-generally not possible to directly discern reasons for high values of this
-compound travel statistic. Urban Analyst nevertheless provides direct insight
-into all individual values, as well as all pairwise combinations of values,
-permitting indirect insight.
+fast travel times and short intervals between services. Low values may
+accordingly always be interpreted as indicating overall good transport
+services. In contrast, high values may arise through various combinations of
+variables, from extremely high values of one single variable, to less extreme
+combinations of the two variables. It is thus generally not possible to
+directly discern reasons for high values of this compound travel statistic.
+Urban Analyst nevertheless provides direct insight into all individual values,
+as well as all pairwise combinations of values, permitting indirect insight.
 
 ## Population density