What does this research mean for the field?

Coworking spaces in Jeddah exhibit pronounced multiscale spatial clustering and are primarily located in business-intensive, highly urbanized, and accessible areas of the metropolitan region. Novelty: ClaimNovelty.INCREMENTAL. Consensus alignment: ConsensusAlignment.NEUTRAL.

What question did this study set out to answer?

To examine the spatial clustering patterns and built-environment locational determinants of coworking spaces across multiple spatial scales.

September 15, 2026Urban ScienceOpen Access

Spatial Clustering and Locational Correlates of Coworking Spaces in Jeddah, Saudi Arabia: A Multiscale GIS and Logistic Regression Analysis

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Authors

AHApri Zulmi Hardi ATAlok Tiwari ANAmmar Naji

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Overview

Spatial modeling reveals significant clustering of coworking spaces across urban business cores, indicating strong reliance on commercial density, built environment density, and road access.

Key Points

To examine the spatial clustering patterns and built-environment locational determinants of coworking spaces across multiple spatial scales.
Mapped and analyzed 53 coworking spaces in Jeddah using point-of-interest, built-up land cover, and road-network GIS datasets.
Assessed spatial distribution patterns using Monte Carlo nearest-neighbour analysis, kernel density estimation, and Ripley’s L-function across distances of 0.25 to 10 km.
Modeled the probability of coworking space presence using binary logistic generalized linear regression at a primary 1 km grid scale, alongside a 2 km grid sensitivity check.
Coworking spaces exhibited significant clustering, with an observed mean nearest-neighbour distance of 1023 m compared to 2667 m under complete spatial randomness (nearest-neighbour ratio = 0.384, Monte Carlo p = 0.001), confirmed across 0.25–10 km scales by Ripley’s L-function.
At the 1 km scale, presence was positively associated with business density (OR = 2.13, p < 0.001), built-up proportion (OR = 1.042 per percentage point, p < 0.001), and road density (OR = 1.075, p = 0.008).
Sensitivity testing at the 2 km scale showed that business density and built-up land remained significant predictors, whereas the association with road density diminished.

Cite This Study

Hardi et al. (2026) studied this question.

synapsesocial.com/papers/6aa913a29013453be30a1c01 https://doi.org/10.3390/urbansci10090527

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September 15, 2026Urban ScienceOpen Access

Spatial Clustering and Locational Correlates of Coworking Spaces in Jeddah, Saudi Arabia: A Multiscale GIS and Logistic Regression Analysis

View Full Paper

Ask AI

Bookmark

Authors

AHApri Zulmi Hardi ATAlok Tiwari ANAmmar Naji

Discussion

Member takes

Overview

Spatial modeling reveals significant clustering of coworking spaces across urban business cores, indicating strong reliance on commercial density, built environment density, and road access.

Key Points

To examine the spatial clustering patterns and built-environment locational determinants of coworking spaces across multiple spatial scales.
Mapped and analyzed 53 coworking spaces in Jeddah using point-of-interest, built-up land cover, and road-network GIS datasets.
Assessed spatial distribution patterns using Monte Carlo nearest-neighbour analysis, kernel density estimation, and Ripley’s L-function across distances of 0.25 to 10 km.
Modeled the probability of coworking space presence using binary logistic generalized linear regression at a primary 1 km grid scale, alongside a 2 km grid sensitivity check.
Coworking spaces exhibited significant clustering, with an observed mean nearest-neighbour distance of 1023 m compared to 2667 m under complete spatial randomness (nearest-neighbour ratio = 0.384, Monte Carlo p = 0.001), confirmed across 0.25–10 km scales by Ripley’s L-function.
At the 1 km scale, presence was positively associated with business density (OR = 2.13, p < 0.001), built-up proportion (OR = 1.042 per percentage point, p < 0.001), and road density (OR = 1.075, p = 0.008).
Sensitivity testing at the 2 km scale showed that business density and built-up land remained significant predictors, whereas the association with road density diminished.

Cite This Study

Hardi et al. (2026) studied this question.

synapsesocial.com/papers/6aa913a29013453be30a1c01 https://doi.org/10.3390/urbansci10090527

View Full Paper

Ask AI

Bookmark