From 6f17744cc118c1198f73f977ee5c1cabf14cd2be Mon Sep 17 00:00:00 2001 From: mpadge Date: Thu, 10 Aug 2023 13:36:04 +0200 Subject: [PATCH] update description of compound transport var for mpadge/UrbanAnalyst#38 --- src/example.md | 9 ++++----- src/variables.md | 20 ++++++++++---------- 2 files changed, 14 insertions(+), 15 deletions(-) diff --git a/src/example.md b/src/example.md index cc0c64b..af60eca 100644 --- a/src/example.md +++ b/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 diff --git a/src/variables.md b/src/variables.md index 2c6b5b5..6700bbd 100644 --- a/src/variables.md +++ b/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