Crowd-Sourced City Images: Decoding Multidimensional Interaction between Imagery Elements with Volunteered Photos

The built environment reshapes various scenes that can be perceived, experienced, and interpreted, which are known as city images. City images emerge as the complex composite of various imagery elements. Previous studies demonstrated the coincide between the city images produced by experts with prio...

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Autores principales:	Yao Shen, Yiyi Xu, Lefeng Liu
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	city image imagery area geotagged photos crowdsourcing image recognition Geography (General) G1-922
Acceso en línea:	https://doaj.org/article/ba866e4881ef4fefb2a8071d9b8673a2
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id	oai:doaj.org-article:ba866e4881ef4fefb2a8071d9b8673a2
record_format	dspace
spelling	oai:doaj.org-article:ba866e4881ef4fefb2a8071d9b8673a22021-11-25T17:52:52ZCrowd-Sourced City Images: Decoding Multidimensional Interaction between Imagery Elements with Volunteered Photos10.3390/ijgi101107402220-9964https://doaj.org/article/ba866e4881ef4fefb2a8071d9b8673a22021-11-01T00:00:00Zhttps://www.mdpi.com/2220-9964/10/11/740https://doaj.org/toc/2220-9964The built environment reshapes various scenes that can be perceived, experienced, and interpreted, which are known as city images. City images emerge as the complex composite of various imagery elements. Previous studies demonstrated the coincide between the city images produced by experts with prior knowledge and that are extracted from the high-frequency photo contents generated by citizens. The realistic city images hidden behind the volunteered geo-tagged photos, however, are more complex than assumed. The dominating elements are only one side of the city image; more importantly, the interactions between elements are also crucial for understanding how city images are structured in people’s minds. This paper focuses on the composition of city image–the various interactions between imagery elements and areas of a city. These interactions are identified as four aspects: co-presence, hierarchy, heterogeneity, and differentiation, which are quantified and visualized respectively as correlation network, dendrogram, spatial clusters, and scattergrams in a framework using scene recognition with volunteered and georeferenced photos. The outputs are interdependent elements, typologies of elements, imagery areas, and preferences for groups, which are essential for urban design processes. In the application in Central Beijing, the significant interdependency between two elements is complex and is not necessarily an interaction between the elements with higher frequency only. The main typologies and the principal imagery elements are different from what were prefixed in the image recognition model. The detected imagery areas with adaptive thresholds suggest the spatially varying spill over effects of named areas and their typologies can be well annotated by the detected principal imagery elements. The aggregation of the data from different social media platforms is proven as a necessity of calibrating the unbiased scope of the city image. Any specific data can hardly capture the whole sample. The differentiation across the local and non-local is found to be related to their preference and activity space. The results provide more comprehensive insights on the complex composition of city images and its effects on placemaking.Yao ShenYiyi XuLefeng LiuMDPI AGarticlecity imageimagery areageotagged photoscrowdsourcingimage recognitionGeography (General)G1-922ENISPRS International Journal of Geo-Information, Vol 10, Iss 740, p 740 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	city image imagery area geotagged photos crowdsourcing image recognition Geography (General) G1-922
spellingShingle	city image imagery area geotagged photos crowdsourcing image recognition Geography (General) G1-922 Yao Shen Yiyi Xu Lefeng Liu Crowd-Sourced City Images: Decoding Multidimensional Interaction between Imagery Elements with Volunteered Photos
description	The built environment reshapes various scenes that can be perceived, experienced, and interpreted, which are known as city images. City images emerge as the complex composite of various imagery elements. Previous studies demonstrated the coincide between the city images produced by experts with prior knowledge and that are extracted from the high-frequency photo contents generated by citizens. The realistic city images hidden behind the volunteered geo-tagged photos, however, are more complex than assumed. The dominating elements are only one side of the city image; more importantly, the interactions between elements are also crucial for understanding how city images are structured in people’s minds. This paper focuses on the composition of city image–the various interactions between imagery elements and areas of a city. These interactions are identified as four aspects: co-presence, hierarchy, heterogeneity, and differentiation, which are quantified and visualized respectively as correlation network, dendrogram, spatial clusters, and scattergrams in a framework using scene recognition with volunteered and georeferenced photos. The outputs are interdependent elements, typologies of elements, imagery areas, and preferences for groups, which are essential for urban design processes. In the application in Central Beijing, the significant interdependency between two elements is complex and is not necessarily an interaction between the elements with higher frequency only. The main typologies and the principal imagery elements are different from what were prefixed in the image recognition model. The detected imagery areas with adaptive thresholds suggest the spatially varying spill over effects of named areas and their typologies can be well annotated by the detected principal imagery elements. The aggregation of the data from different social media platforms is proven as a necessity of calibrating the unbiased scope of the city image. Any specific data can hardly capture the whole sample. The differentiation across the local and non-local is found to be related to their preference and activity space. The results provide more comprehensive insights on the complex composition of city images and its effects on placemaking.
format	article
author	Yao Shen Yiyi Xu Lefeng Liu
author_facet	Yao Shen Yiyi Xu Lefeng Liu
author_sort	Yao Shen
title	Crowd-Sourced City Images: Decoding Multidimensional Interaction between Imagery Elements with Volunteered Photos
title_short	Crowd-Sourced City Images: Decoding Multidimensional Interaction between Imagery Elements with Volunteered Photos
title_full	Crowd-Sourced City Images: Decoding Multidimensional Interaction between Imagery Elements with Volunteered Photos
title_fullStr	Crowd-Sourced City Images: Decoding Multidimensional Interaction between Imagery Elements with Volunteered Photos
title_full_unstemmed	Crowd-Sourced City Images: Decoding Multidimensional Interaction between Imagery Elements with Volunteered Photos
title_sort	crowd-sourced city images: decoding multidimensional interaction between imagery elements with volunteered photos
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/ba866e4881ef4fefb2a8071d9b8673a2
work_keys_str_mv	AT yaoshen crowdsourcedcityimagesdecodingmultidimensionalinteractionbetweenimageryelementswithvolunteeredphotos AT yiyixu crowdsourcedcityimagesdecodingmultidimensionalinteractionbetweenimageryelementswithvolunteeredphotos AT lefengliu crowdsourcedcityimagesdecodingmultidimensionalinteractionbetweenimageryelementswithvolunteeredphotos
_version_	1718411883847876608

Crowd-Sourced City Images: Decoding Multidimensional Interaction between Imagery Elements with Volunteered Photos

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