Abstract:
Among the public services offered by the government of Sri Lanka, public parks can be
identified as one of the crucial elements. With the increasing trend of building new public
parks, it is necessary to identify the factors that may contribute to the optimum usage of
parks. Literature on public park usage mostly discusses on impact of non-locational factors
such as design elements, behavioral and psychological factors on the functionality.
Therefore, this study aims to develop an approach to optimize public park location
decisions based on functional efficiency. First, the usage of public parks was determined
by standardizing the google visit data based on aggregated and anonymized data from
users who have opted to allow access to Google Location History. Second, a questionnaire
survey with a total sample of 165 park users was carried out to develop a user profile for
parks and to understand its relationship with usage. Then the factors, which correlate with
park usage, were used to define the factors of recreational attractiveness. These factors
are share of population in the age group of 15 to 24, share of population in the age group
of 45 to 65, share of population in the major ethnic group, street connectivity, park size
and number of competitors in the neighborhood. In order to calculate the recreational
gravity of each public park, data was obtained on defined attractiveness factors from each
Grama Niladhari Division in the market range of each park and the distance from each
GND to each park. Next, it modeled the gravitation relationship between usage and
recreational gravity of each case study. Finally, the applicability of the derived model was
tested with three additional cases. Accordingly, the model predicts the functionality with
-15% variance for moderate size parks, -25% variance for small parks, and -35% variance
for large parks with R2 of 0.66. Therefore, the model needs extension with some additional
factors. Importantly, it highlights the importance of considering locational factors along
with non-locational factors to optimize the public park location decisions.
