{"id":7980,"date":"2026-08-14T08:00:00","date_gmt":"2026-08-14T06:00:00","guid":{"rendered":"https:\/\/photoni.st\/?p=7980"},"modified":"2026-08-02T09:43:10","modified_gmt":"2026-08-02T07:43:10","slug":"street-photography-taxonomy","status":"publish","type":"post","link":"https:\/\/photoni.st\/index.php\/2026\/08\/14\/street-photography-taxonomy\/","title":{"rendered":"Street Photography Taxonomy"},"content":{"rendered":"\n

Street photography is something that bothers me<\/a>, so I want to understand it. And to understand things, I need to classify them and understand how they relate. Call it professional bias.<\/p>\n\n\n\n

With street photography, I quickly hit a wall: the term clearly covers many disparate activities and everybody has a point of view on what it really is about. So when I tried to classify it, I realised that I couldn\u2019t create a neat and orderly way of breaking things down using the clear known categories.<\/p>\n\n\n

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\"\"<\/a>
Street photography taxonomy<\/figcaption><\/figure>\n<\/div>\n\n\n\n\n\n\n

My starting point was the categories I\u2019ve always used: urban, architecture, street portraits, street activity, events. But these didn\u2019t seem to always work with the photographs I was looking at. For example, an empty alley at dusk where the light is the subject isn\u2019t architecture or urban, exactly. A candid portrait of a stranger and a carefully composed shot of someone who knows you\u2019re there aren\u2019t really the same category either.<\/p>\n\n\n\n

It became clear that calling all these things street photography is like calling every book a novel: it will work sometimes, but it will fail most of the time. Or calling every song a ballad: you lose a lot of precision once you start looking at what exists.<\/p>\n\n\n\n

Szarkowski got some of the way there with his His five categories (The Thing Itself, The Detail, The Frame, Time, Vantage Point) published in The Photographer\u2019s Eye<\/em>. But five isn\u2019t enough once you start looking at details. His point of view was historical more than diagnostic, so I have a different purpose. I wanted something that could describe any street photo, including ones that don\u2019t exist yet, so I built a taxonomy with fourteen axes (I did my PhD around taxonomies).<\/p>\n\n\n\n

Here\u2019s the list:<\/p>\n\n\n\n

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  1. Population: empty to crowded. How many people are in the frame.<\/li>\n\n\n\n
  2. Human role: primary subject, secondary element, or absent. Are people what the photo is about, or just part of the scene.<\/li>\n\n\n\n
  3. Architectural presence: absent to background to dominant grid. Is architecture the stage, or the performer.<\/li>\n\n\n\n
  4. Photographer relationship: unaware candid, aware\/acknowledging, or directed\/staged. Does the subject know you\u2019re there.<\/li>\n\n\n\n
  5. Distance: intimate (under 1m), portrait (1-3m), or environmental (3m+). The physical space between you and what you\u2019re shooting.<\/li>\n\n\n\n
  6. Framing structure: geometric and ordered versus dense and organic. Is the frame built on lines and symmetry, or layered and chaotic.<\/li>\n\n\n\n
  7. Temporal peak: a single decisive instant, or no peak moment at all.<\/li>\n\n\n\n
  8. Colour: monochrome, muted, or saturated and driven.<\/li>\n\n\n\n
  9. Atmospheric weight: documentary and transparent versus atmospheric and painterly.<\/li>\n\n\n\n
  10. Light source: natural ambient, mixed, or artificial\/introduced flash.<\/li>\n\n\n\n
  11. Motion: frozen, blurred, or motion as the subject itself.<\/li>\n\n\n\n
  12. Juxtaposition: absent or present. Is there an unexpected collision in the frame.<\/li>\n\n\n\n
  13. Narrative density: single subject, layered scene, or abstract and non-representational.<\/li>\n\n\n\n
  14. Intention: found and observed, hunted and anticipated, or constructed and set up.<\/li>\n<\/ol>\n\n\n\n

    Each axis has a spectrum of answers. A vector across all fourteen describes any street photo more precisely than the word \u201cstreet photography\u201d ever could.<\/p>\n\n\n\n

    After testing this classification on a number of photos, I realised that there are well defined clusters and these already have names. For example:<\/p>\n\n\n\n