The impact of the built environment on creativity in public spaces of Dutch university campuses and science parks

Figures & data

Figure 1. Location of cities and campuses: (a) Amsterdam, Utrecht, and Groningen; (b) location of inner-city campuses and SPs in each city.

Figure 2. Visualization of public spaces typologies.

Table 1. Number of VGI responses and frequency of creative encounters per campus

Number of Valid Responses per Campus				Frequency of creative encounters per 100x100m cell at each campus
Campus	Respondents	Polygon responses	Number of 100 × 100 m Cells Within the Study Area	Min	Max	Mean
Amsterdam Center (UVA_Centre)	60	83	430	1	35	3.4
Utrecht Center (UU_Centre)	49	75	192	1	53	4.1
Groningen Centre (RUG Centre)	65	86	89	1	40	5.7
Amsterdam Science Park (ASP)	105	166	68	1	44	11.1
Utrecht Science Park (USP)	104	187	186	1	40	8.8
Zernike Campus Groningen	98	147	167	1	43	6.9
Total	481	744	1132	-	-	-

Table 2. Explanatory variables

Explanatory Variable	Unit	What the data represents?	Description of the base layer
DISTANCE TO: Urban Functions
DIST_Buildings	Polygons	Inner-city: all academic and research buildings SPs: all buildings within the institutional boundaries. For both types of campus, this includes indoor public spaces.	BGT Top10.NL
DIST_RestCafeCanteen	Points	Points representing locations of restaurants, canteens and cafés Inner-city: points within 800 m radius SPs: points within the institutional boundaries	Top10NL OSM Observation
Public spaces
DIST_StreetsSidewalks	Polygons	Walkable streets and sidewalks	BGT
DIST_SBB (Spaces between buildings)	Polygons	Open public: spaces that everyone can access Semi-public: limited access via doors/gates	BGT
DIST_Green	Polygons	Urban parks, gardens and areas with grass	BGT
DISTANCE TO: Physical features
DIST_TablesBenches	Points	Points representing sitting opportunities (tables and benches)	BGT, OSM, observation and Amsterdam database (2020)
DIST_Water	Polygons	Canals and lakes	BGT
DIST_Vegetation	Points	Trees and small green features	BGT

Table 3. Generalized negative binomial linear model

	Parameter Estimates
	Coefficient	Wald Chi-Square	df	Sig.
(Intercept)	1.724	344.543	1	.000
DIST_Buildings	−.003	236.762	1	.000
DIST_RestCafeCanteen	−.003	93.723	1	.000
DIST_StreetsSidewalks	.004	.937	1	.333
DIST_SBB	−.003	10.566	1	.001
DIST_Green	−.001	1.597	1	.206
DIST_TablesBenches	−.002	17.600	1	.000
DIST_Water	.001	7.529	1	.006
DIST_Vegetation	.002	22.862	1	.000
ASP_Dummy	1.051	69.770	1	.000
UVACentre_Dummy	.585	27.051	1	.000
USP_Dummy	.880	67.451	1	.000
UUCentre_Dummy	.241	5.166	1	.023
Zernike_Dummy	.445	15.346	1	.000
RUGCentre_Dummy (reference outcome)	0^a	-	-	-

a. Set to zero because this parameter is redundant.

Table 4. Generalized negative binomial linear models: Comparison between inner-city campuses and SPs

Parameter Estimates
	Inner-city campuses (N = 711)				Science Parks (N = 421)
	Coefficient	Wald Chi-Square	df	Sig.	Coefficient	Wald Chi-Square	df	Sig.
(Intercept)	2.101	356.978	1	.000	2.075	412.729	1	.000
DIST_Buildings	−.003	257.450	1	.000	.001	.631	1	.427
DIST_RestCafeCanteen	−.002	6.964	1	.008	−.004	56.672	1	.000
DIST_StreetsSidewalks	.001	.004	1	.947	.005	.770	1	.380
DIST_SBB	−.003	9.324	1	.002	−.008	4.514	1	.034
DIST_Green	.000	.838	1	.360	−.004	.322	1	.570
DIST_TablesBenches	−.001	3.117	1	.077	−.003	17.886	1	.000
DIST_Water	.001	1.204	1	.273	−.002	1.035	1	.309
DIST_Vegetation	−.002	7.287	1	.007	.004	53.840	1	.000
Amsterdam_Dummy	.165	1.573	1	.210	.794	44.948	1	.000
Utrecht_Dummy	−.152	1.552	1	.213	.593	32.749	1	.000
Groningen Dummy (reference outcome)	0^a	-	-	-		-	-	-

a. Set to zero because this parameter is redundant.

Figure 3. Frequency of responses per 100x100m grid-cell: (a) total dataset; (b) Inner-city; (c) SPs.

Figure 4. Mapped frequency of creative encounters per campus, aggregated to 100 × 100 grid-cells.