2.4. Analysis
Results from the questionnaire were analysed with IBM SPSS
Statistics for Windows 21.0. Prior to analysis, the 5 skill levels
(1 ¼ complete beginner; 2 ¼ advanced beginner; 3 ¼ moderately
experienced; 4 ¼ very experienced; 5 ¼ experts) for mountain
bikers were re-classified by merging category 1 (‘complete
beginner’) and category 2 (‘advanced beginner’) into one category
(‘beginner’) to account for their smaller sample size.
To compare frequency data, such as the socio-demographics
and rider characteristics (i.e., skill level and field vs. online), Pearson's
chi-square tests were applied. The rating-scale data were
analysed with analysis of variance (ANOVA) including skill level
and sampling mode (field vs. online). ANOVA was also applied to
continuous variables measuring self-estimated average distances
travelled to tracks in the Northern Sydney area. Open-ended
questions were analysed qualitatively by identifying the major
categories/themes emerging from participants' comments.
We used several spatial scales (‘planning areas’) in our analysis
to demonstrate the versatility of PPGIS mapping and associated GIS
analysis including inside vs. outside national parks. Tracks were
assigned to either being located ‘in park’ or ‘outside’ if most (>90%)
of the track length was located in either category, otherwise, a track
was considered located in ‘both’.
Markers collected in the field were transferred by a researcher
onto Google map and then exported together with the online
mapping data for import into a geographical information system
(ArcGIS 10.1). An attribute field was added to the database that
coded whether data had been collected in the field or online. Prior
to the data analysis, we excluded any markers placed outside the
study area (6.8%). A few mountain bikers (5%) were excluded
entirely from the analysis (both the survey and PPGIS mapping)
because they indicated in the survey they never ride in the