Best of the web: Predicting gentrification with data, cities and mental health, the cost of deforestation

    by Best of the Web
    Wednesday, 6 September 2017 14:30 BST

Nancy McBride's cat looks out from an air hole punched in a tub after the cat was found in her garage when McBride came home for the first time since Harvey floodwaters arrived in Houston, Texas September 1, 2017. REUTERS/Rick Wilking

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Residents of most major Western cities are by now familiar with the early, tell-tale signs of gentrification. Where boutique clothes stores, trendy coffee shops and favourable reviews in culture supplements appear, hiking rental prices are not far behind.

But by the time a neighbourhood is acknowledged as ‘up and coming’ it may already be too late for families and long-term residents who can no longer afford to live in their own communities.

So how can we predict the sites and speed of gentrification before it shows itself? Big data may provide the answer, according to Adam Frank, writing for NPR:

“From cellphones to credit card transactions to social media, we are all leaving digital contrails of almost all of our activity in the world... And seeing hidden patterns in gentrification may be exactly the kind of task big data and data science are best at.”

Obvious sources for relevant data are house prices, eviction rates and demographic statistics gleamed from censuses. But even social media data can be purposed to track changes in a geographical area over time.

In the US city of Louisville, analysing the movements of Twitter users by their geo-tags showed mobility patterns in different neighbourhoods. The results indicated that Ninth Street - seen as the traditional divide between the poorer, African-American community to the west and wealthier white community to the east – was no barrier to those living in the former, who were found to be more mobile across the whole city.

By observing which communities see growing or declining rates of movement from other areas, planners can observe trends of gentrification or decline:

“By analyzing these patterns over months or years, it may be possible to see the "signal" of gentrification appear as people who normally would not be visiting a neighborhood begin making more frequent appearances.”

Preventative action can then be taken to ward off gentrification – constructing affordable housing, strengthening tenants’ rights and encouraging extensions to small business leases.

But the revelations of big data can swing both ways – just as democratised data can allow residents to better understand and prepare for trends within their own community, it can also be used by speculators and developers to speed up investment while prices remain low.

-Ruairi Casey

Discover more top stories from around the web this week. Have we missed anything? Tweet at @mjponsford or email place@thomsonreuters.com.

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