KAJIAN KARAKTERISTIK SPASIAL YANG MEMENGARUHI POLA PEMILIHAN LOKASI COWORKING SPACES DI KOTA BANDUNG

The advent of the 21st century made rapid development of information, communication, and technology (ICT). This has influenced new digital lifestyles and economic structures, including new workstyles and aspects of urban planning. Digital natives, society who lives with technology, have characterist...

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Bibliographic Details
Main Author: Muhammad Fachryza, Dimas
Format: Final Project
Language:Indonesia
Online Access:https://digilib.itb.ac.id/gdl/view/48758
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Institution: Institut Teknologi Bandung
Language: Indonesia
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Summary:The advent of the 21st century made rapid development of information, communication, and technology (ICT). This has influenced new digital lifestyles and economic structures, including new workstyles and aspects of urban planning. Digital natives, society who lives with technology, have characteristics of being brave against conventional cultures, such as hierarchical and rigid, and bring out a new style of working. Nowadays, it is possible for digital natives to become digital nomads who have the opportunity to work anywhere and anytime, including from home. However, digital nomads need 3rd spaces for socialization to avoid the problem of social isolation. In addition, the economic trends of cities in the world are moving towards a knowledge-based economy (KBE). This dynamism of civilization, both in terms of lifestyle and restructuring of the global economy has led to the idea of working in Coworking Spaces (CwS). The rising of CwS has become a global phenomenon, including in Indonesia, marked by the establishment of CwS in Bandung City. Tobler’s first law of geography can explain the basic understanding of CwS's emergence and spatial patterns as an additional feature for urban planning documents. The purpose of this research is to analyze the distribution of CwS location patterns and association with spatial characteristics, using location patterns & distribution analysis (global moran’s I, kernel density, and descriptive statistics) and somer’s d. This research uses secondary data collection methodology from OpenStreetMap (OSM), google maps, social media, and related articles. The result shows that CwS location pattern forms clusters to achieve economies of scale and tends to be located close to activity café & coffee shops, bar & pub, spatial plan: education area, and parks & sports facilities. This is in accordance with the people's clustering hypothesis (creative class and consumer city).