Koichi Ito

Koichi Ito
  • Master of Science
  • PhD Student at National University of Singapore

Looking for collaboration opportunities in urban analytics

About

15
Publications
12,402
Reads
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916
Citations
Introduction
Interested in urban computational analysis, especially with perceptual data, such as street view imagery
Skills and Expertise
Current institution
National University of Singapore
Current position
  • PhD Student
Additional affiliations
April 2021 - present
World Bank
Position
  • Consultant
Description
  • I conduct spatial data collection/analysis on socioeconomic indicators in African countries.
July 2020 - May 2021
National University of Singapore
Position
  • Master's Student
Description
  • As a research assistant, I conducted a systematic review on the application of street view imagery in urban analytics and GIS.
Education
August 2019 - June 2021
National University of Singapore
Field of study
  • Urban Planning

Publications

Publications (15)
Preprint
Full-text available
Understanding people's preferences and needs is crucial for urban planning decisions, yet current approaches often combine them from multi-cultural and multi-city populations, obscuring important demographic differences and risking amplifying biases. We conducted a large-scale urban visual perception survey of streetscapes worldwide using street vi...
Article
Full-text available
Urban visual perception is important for the human experience in cities, shaped by intertwined characteristics of urban landscapes. By quantifying and explaining these perceptual experiences, researchers can gain insights into human preferences and support decision-making in planning and design. However, past studies have shown inconsistencies in s...
Preprint
Full-text available
Three-dimensional urban environment simulation is a powerful tool for informed urban planning. However, the intensive manual effort required to prepare input 3D city models has hindered its widespread adoption. To address this challenge, we present VoxCity, an open-source Python package that provides a one-stop solution for grid-based 3D city model...
Article
Full-text available
Street view imagery (SVI) has been instrumental in many studies in the past decade to understand and characterize street features and the built environment. Researchers across a variety of domains, such as transportation, health, architecture, human perception, and infrastructure have employed different methods to analyze SVI. However, these applic...
Preprint
Full-text available
Street view imagery (SVI) has been instrumental in many studies in the past decade to understand and characterize street features and the built environment. Researchers across a variety of domains, such as transportation, health, architecture, human perception, and infrastructure have employed different methods to analyze SVI. However, these applic...
Article
Full-text available
Street-view-based techniques for assessing the sky view factor (SVF) and solar irradiance under trees are gaining attention as tools for evaluating trees as nature-based solutions to mitigate urban heat risks. Although these metrics significantly depend on the morphology of trees and resulting canopy transmittance, an existing approach, termed the...
Article
Full-text available
Cycling is vital for sustainable and healthy cities. To encourage such activities, understanding urban bikeability at both detailed and broad spatial scales is crucial. Street view imagery (SVI) offers in-depth insights into how street features influence micro-mobility patterns, but existing studies are mainly correlational. This research utilized...
Article
Full-text available
Street view imagery (SVI), an emerging geospatial dataset, is useful for evaluating active transportation infrastructure, but it faces potential biases from its vehicle-based capture method, diverging from pedestrians’ and cyclists’ perspectives. Existing literature lacks both an examination of these biases and a solution. This study identifies and...
Article
Full-text available
Street view imagery (SVI) is instrumental for sensing urban environments, benefitting numerous domains such as urban morphology, health, greenery, and accessibility. Billions of images worldwide have been made available by commercial services such as Google Street View and crowdsourcing services such as Mapillary and KartaView where anyone from any...
Article
Full-text available
Visual characteristics of the built environment affect how people perceive and experience cities. For a long time, many studies have examined visual perception in cities. Such efforts have accelerated in recent years due to advancements in technologies and the proliferation of relevant data (e.g., street view imagery, geo-tagged photos, videos, vir...
Article
Full-text available
This paper brings a comprehensive systematic review of the application of geospatial artificial intelligence (GeoAI) in quantitative human geography studies, including the subdomains of cultural, economic, political, historical, urban, population, social, health, rural, regional, tourism, behavioural, environmental and transport geography. In this...
Article
Full-text available
The well-being of residents is considerably influenced by the quality of their environment. However, due to the lack of large-scale quantitative and longitudinal evaluation methods, it has been challenging to assess residents' satisfaction and achieve social inclusion goals in neighborhoods. We develop a novel cost-effective method that utilizes ti...
Article
Full-text available
Street view imagery has rapidly ascended as an important data source for geospatial data collection and urban analytics, deriving insights and supporting informed decisions. Such surge has been mainly catalysed by the proliferation of large-scale imagery platforms, advances in computer vision and machine learning, and availability of computing reso...
Article
Full-text available
Studies evaluating bikeability usually compute spatial indicators shaping cycling conditions and conflate them in a quantitative index. Much research involves site visits or conventional geospatial approaches, and few studies have leveraged street view imagery (SVI) for conducting virtual audits. These have assessed a limited range of aspects, and...
Preprint
Full-text available
Studies evaluating bikeability usually compute spatial indicators shaping cycling conditions and conflate them in a quantitative index. Much research involves site visits or conventional geospatial approaches, and few studies have leveraged street view imagery (SVI) for conducting virtual audits. These have assessed a limited range of aspects, and...

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