Abstract

AbstractWith a rapid development of data-driven technologies, many opportunities have arisen to understand and characterize urban contexts. This paper addresses the methodology to understand a place in urban settings through the lens of third places and motility based on the walkable distance. To capture and process third-place data, fetched from Google Places, based on a given location, this paper discuses two data structures and process of discrete and continuous data. Representation of third places in a specific location of a city is characterized by representative queries. Its identified chart as a perspective of understanding a designated area could compare with other charts in different places. This method allows us to distinguish the constitution of third places based on the distance among places, enabling us to develop design strategies to differentiate or accord the sites based on mobility. The goal is to set up a method to process, interpolate, and visualize discrete and continuous urban data with representative queries of third places based on distance.

Highlights

  • Third place refers to the social environments between the two usual surroundings of the home as a first place and the workplace as second place

  • According to the book “The third place”, an engagement location, is where people consider it as a measurement of their sense of distinctiveness and wholeness

  • Even we could know the sequences of the differences between two particular points in the same cities based on the walkable distance

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Summary

Background

Third place refers to the social environments between the two usual surroundings of the home as a first place and the workplace as second place. The benefits of third-place involvement are discussed regarding diversity and novelty, emotional expressiveness, color, and perspective. This means that the third-place shows a section of a city in many ways. Even we could know the sequences of the differences between two particular points in the same cities based on the walkable distance. This lens is useful for understanding the context of an urban scale and characterizes the relationship at the architectural level. With the measuring system of walkable distance, urban contexts would be reconsidered with the lens of third place

Data and Data Structure for Manipulation
Pixel Structure for Continuous Data and Blending Data with Neighbors
Parse Third Place Data and Visualization Based on Google
Generate Data Structures and Inspect with Visualizations
Discussion
Future Work

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