4  Walkability

This analysis considers local walkability by calculating travel times to the closest public playground. Playgrounds are a useful candidate to assess the potential of walking to a nearby amenity as they are distributed evenly through the city with an average catchment of 600m (Figure 4.1).

Figure 4.1: Locations of council playgrounds designed with a 600m catchment.

4.1 Distance and speed on hilly terrain

Distance-based planning is convenient as it’s easy to calculate and objective. However, it doesn’t translate well to people’s decision making which are typically time-centric as well as accounting for some or all of the 5Cs of walkability (Chapter 1). Hence, even simple distance to time conversions can be more informative to the layperson planning a walking trip to the park. Figure 4.2 shows heatmaps of walkability to the nearest playground as travel time. The flat assumption converts from distance to time using a speed of 5km/h - a common conversion based on a fit adult e.g. Section 3.4 in NZTA pedestrian planning and design guide. The flat assumption heatmap in Figure 4.2 can be considerably improved with Tobler’s function that accounts for changing walking speed by slope (Section 3.2). Though not easily visible in these heatmaps, adjusting speed by slope results in an average increase in travel time of 10% [1].

Figure 4.2: Walkability maps coloured as both distance (m) and time (minutes).

Heatmaps like Figure 4.2 are useful for a holistic picture but lack connection to the average citizen’s day to day life. Model outputs can reduce the visual complexity of the heatmaps to summaries by a well-understood spatial entity - suburbs. Walking times are simplified with a hierarchical Bayesian model (truncated normal) [2] giving a summary of (1) average walking times and spread in walking times by suburb and (2) average of averages and spread for the whole city.

The forest plot in Figure 4.3 summarising the Bayesian model can be summarised even further with a quadrant classification. We can get the suburbs that lie in the four quadrants with some simple data filters. Popular suburbs of the city are listed from best to worst (in terms of local walkability to playgrounds).

Figure 4.3: Forest plot showing average (μ\mu) and spread (σ\sigma) in walking time to the nearest playground. The grey shaded area gives the average for the whole city.

High μnorm\mu_{norm} Low μnorm\mu_{norm}
Low σnorm\sigma_{norm} Consistently good walkability Consistent but poor walkability
High σnorm\sigma_{norm} Poor walkability for most areas Good walkability for some areas
suburb quadrant characteristic
Te Aro Low σ\sigma and μ\mu Consistently good walkability
Newtown Low σ\sigma and μ\mu Consistently good walkability
Pipitea Low σ\sigma; High μ\mu Consistent but poor walkability
Hataitai Low σ\sigma; High μ\mu Consistent but poor walkability
Newlands High σ\sigma; Low μ\mu Good walkability for some areas
Tawa High σ\sigma; Low μ\mu Good walkability for some areas
Brooklyn High σ\sigma; Low μ\mu Good walkability for some areas
Khandallah High σ\sigma and μ\mu Poor walkability for most areas
Karori High σ\sigma and μ\mu Poor walkability for most areas