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... Nobody wants to live in a bad neighbourhood but, alas, some of us do. It is inevitable: where there are ‘better’ neighbourhoods, there must also be neighbourhoods which are deemed to be poor, unsafe, or even dangerous. However, such perceptions can change over time: some neighbourhoods ‘improve’ through the efforts of city councils, investors, and inhabitants; whereas others slowly become dilapidated for various reasons. It is generally very difficult to assess the quality of a neighbourhood. Such ‘verdicts’ are mostly based on one or several personal opinion(s). But what if socially constructed views are aggregated into datasets that are immune from the wear and tear of time? On the one hand, there could be a more accurate, evidence-based view on local conditions. On the other hand, inhabitants and local businesses could become trapped in stigmas based on datasets outside of their control. Does such data merely reflect a social situation in a public space, or does it amount to personal data? This question merits attention as companies and local governments are starting to collect, store, analyse, and publicize such data, specifically related to feelings of fear and insecurity.
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Stored only when both papers are in the corpus — 12 citations exist upstream.
Papers that cite the same references — the relational fallback while embeddings are still backfilling.
Imported from OpenAlex (CC0) — nothing here is generated.