🌌🏞🎑🌇🌁🔭

The poor image is a copy in motion. Its quality is bad, its resolution substandard. As it accelerates, it deteriorates. It is a ghost of an image, a preview, a thumbnail, an errant idea, an itinerant image distributed for free, squeezed through slow digital connections, compressed, reproduced, ripped, remixed, as well as copied and pasted into other channels of distribution.

–– Steyerl, H. (2012). In Defense of the Poor Image. In The Wretched of the Screen. Berlin: Sternberg Press.

Image Explorer is a set of browser-based tools for the visual exploration of image collections.

🥞 Image Stack

Enables the creation of an "image stack" from a set of image URLs.

Follows from recent work on "composite images" (Pearce et al, 2018; Niederer & Colombo, 2019).

🍉 Image Slice

Enables the creation of an "image slice" from a set of image URLs.

🍹 Image Blend

Enables the creation of an "image blend" from a set of image URLs, including experimentation with different P5 blend modes.

🍱 Image Grid

To do: Enables the creation of an "image grid" from a set of image URLs, including the display of a larger version of the image on click/rollover. See roadmap.

🎒 How to use

😸 On Github

You can use on the web at https://image-explorer.publicdatalab.org/

🧮 On P5 web editor

You can run on P5 web editor, where you can login/create an account and duplicate and customise the code to add your own URLs and change variables.

📦 Download and run locally

You can download the repository, modify P5 files and open using a web browser.

🍴Fork the code on GitHub

You can fork the repository, modify accordingly, preview using GitHub Pages and suggest changes with a pull request.

🎏 How to help

You can see the roadmap here.

🙈 If you get stuck

You can add an issue here.

🐙 Inspiration, acknowledgments and contributors

Image Explorer is inspired by recent work on visual methods in the context of internet studies and new media research. It is created using P5.js and intended to work alongside other web-based tools and libraries for repurposing web and social media data such as those from the Digital Methods Initiative and the médialab, Sciences Po, Paris.

The project was prompted by a set of visual methods recipes from Gabriele Colombo drawing on his doctoral work exploring the design of composite images. These recipes were documented and refined for a module on Digital Methods for Internet Studies: Concepts, Devices and Data convened by Liliana Bounegru and Jonathan Gray at the Department of Digital Humanities, King's College London, leading to a set of collaborative group projects with their students and the European Forest Institute. The approaches behind these recipes draw on several years of experimentation with images in the context of research and teaching at the Visual Methodologies Collective (Amsterdam University of Applied Sciences), the Digital Methods Initiative (University of Amsterdam), DensityDesign Lab (Politecnico di Milano), the médialab (Sciences Po, Paris) and beyond. You can read more about this in the readings listed below, in particular Colombo, 2019; Niederer, 2018; and Niederer & Colombo, 2019.

This initial prototype was developed by Jonathan Gray thanks to support from the Arts & Humanities Research Institute at King's College London and through collaborations and conversations with Gabriele Colombo, Liliana Bounegru and Michele Mauri.

📖 Readings and links

Further readings can be found in the visual methods Zotero bibliography. Further visual methods recipes can be found here.

Colombo, G. (2019). Studying Digital Images in Groups: The Folder of Images. In Advancements in Design Research (pp. 185–195). Milan: FrancoAngeli.
Niederer, S. (2018). Networked Images Visual Methodologies for the Digital Age. Amsterdam: Hogeschool van Amsterdam.
Niederer, S., & Colombo, G. (2019). Visual Methodologies for Networked Images: Designing Visualizations for Collaborative Research, Cross-platform Analysis, and Public Participation. Diseña, 40–67.
Pearce, W., Özkula, S. M., Greene, A. K., Teeling, L., Bansard, J. S., Omena, J. J., & Rabello, E. T. (2018). Visual cross-platform analysis: Digital methods to research social media images. Information, Communication & Society, 0, 1–20.

PublicDataLab/image-explorer

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