Travel distances vary across different areas. For example, we cannot assume that commute distances in London are the same as those in Cambridge.
Matching (#8) is done based on socioeconomic and demographic variables, but individuals/households in different parts of the country that share the same variables may exhibit different travel behaviour due to land use / transport options. If we don't filter, we may end up with travel distances in our study area that are too long, or a mode share that is not representative.
When carrying out matching for any area, we should filter the NTS survey data for that area. Initially I was doing this (see this function, but the sample became to small and matching at the household level notebook resulted in a low matching rate.
Possible workarounds:
Use filter_by_region() but include all regions that would have similar travel patterns to the study area
Use filter_by_region() to filter the NTS to the study area. Apply propensity score matching on the household level, as described in #13
Travel distances vary across different areas. For example, we cannot assume that commute distances in London are the same as those in Cambridge.
Matching (#8) is done based on socioeconomic and demographic variables, but individuals/households in different parts of the country that share the same variables may exhibit different travel behaviour due to land use / transport options. If we don't filter, we may end up with travel distances in our study area that are too long, or a mode share that is not representative.
When carrying out matching for any area, we should filter the NTS survey data for that area. Initially I was doing this (see this function, but the sample became to small and matching at the household level notebook resulted in a low matching rate.
Possible workarounds:
filter_by_region()but include all regions that would have similar travel patterns to the study areafilter_by_region()to filter the NTS to the study area. Apply propensity score matching on the household level, as described in #13