Are They Well Situated? Spatial Analysis of Privately Owned Public Space, Manhattan, New York City

Abstract

In this article, we attempt to examine how privately owned public spaces through incentive zoning have played out as a means to generate public spaces, as part of improving spatial equity in Manhattan, New York City. Using spatial statistical analyses, we find that the inclusion of privately owned public spaces reduces overall average distance to the nearest public space from both working and living population, and helps to balance the distribution of the public spaces throughout the city, by covering the locations where publicly owned public spaces are sparse. More importantly, the location of privately owned public spaces closely aligns with potential demand from the working and tourist population, though not from the resident population. We extend the findings to policy, suggesting that allocation of privately owned public space should be implemented comprehensively rather than discretely, as well as in relationship to publicly owned public spaces, to sharpen their role in achieving fair distribution of public spaces throughout the city.

Big Data Analysis to Observe Check-in Behavior Using Location-Based Social Media Data

“…In this analysis, we partitioned the output cells as 200 × 200 m areas. The cell was partitioned to provide a precise way to measure and compare urban intensity; this method is also applied in many spatial analysis studies [100]. Since the focus of this study is to observe the check-in behavior in Shanghai towards using Weibo and analyze the check-in behavior for males and females to observe the number of males, females, and total Weibo users in Shanghai, we also calculated the frequency of check-ins over the period (time slices) and KDE is a spatial analysis technique that accounts for the location of features (i.e., destination, time) relative to each other and is an emerging spatial tool that has previously been applied [47,60,61,[90][91][92] to the examination of various aspects of social media data analysis such as human activity and mobility patterns [93], check-in behavior [94], defining city boundaries [95,96], and point of interest recommendation [97].…”

“…In this analysis, we partitioned the output cells as 200 × 200 m areas. The cell was partitioned to provide a precise way to measure and compare urban intensity; this method is also applied in many spatial analysis studies [100]. Since the focus of this study is to observe the check-in behavior in Shanghai towards using Weibo and analyze the check-in behavior for males and females to observe the number of males, females, and total Weibo users in Shanghai, we also calculated the frequency of check-ins over the period (time slices) and during weekdays and the weekend.…”

Geo‐spatial analyses in education research: the critical challenge and methodological possibilities

“…GIS thereby visualises data by utilising geo-coded variables, and allows for consideration of patterns and statistics generated by maps that are not necessarily available using traditional statistical approaches (Hogrebe & Tate, 2012). GIS has enabled research on inequitable distribution of public services including medical care centres, playgrounds, parks, and preschools (Knox, 1978;Oh & Jeong, 2007;Shen, 1998;Talen & Anselin, 1998;Yoon & Srinivasan, 2015). Therefore, the utilisation of GIS has allowed researchers to integrate multiple data layers from more contextualised and nuanced viewpoints, and encouraged them to develop rich discourses that go beyond fragmented information about lived experiences.…”

“…Therefore, the utilisation of GIS has allowed researchers to integrate multiple data layers from more contextualised and nuanced viewpoints, and encouraged them to develop rich discourses that go beyond fragmented information about lived experiences. GIS has enabled research on inequitable distribution of public services including medical care centres, playgrounds, parks, and preschools (Knox, 1978;Oh & Jeong, 2007;Shen, 1998;Talen & Anselin, 1998;Yoon & Srinivasan, 2015). Education researchers have used GIS to examine teacher distribution and local-level choice programs (Cobb & Glass, 1999;Parsons et al, 2000;Saporito & Sohoni, 2006;Schultz, 2014;Taylor, 2002), and Tate's (2012b) recent volume highlights advances in GIS for understanding urban contexts (Hogrebe, 2012).…”

The concept of urban intensity and China's townization policy: Cases from Zhejiang Province

“…The purpose of the cell division is to provide a consistent study object for measurement and comparison of urban intensity scores across the selected towns. This method has been applied commonly in other studies of spatial analysis (Yoon & Srinivasan, 2015). The World Imagery, for instance, provided 500 meters resolution for imagery at a small scale, where each pixel represented a five meter by five meter ground distance.…”