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Social Communications in Time and Space

by Ben Anderson on Mar 11, 2011

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Presentation given at ESRC Research Centre on Micro-Social Change (http://www.iser.essex.ac.uk/research/misoc) update workshop. Uses call record data to analyse/predict multiple deprivation and also ...

Presentation given at ESRC Research Centre on Micro-Social Change (http://www.iser.essex.ac.uk/research/misoc) update workshop. Uses call record data to analyse/predict multiple deprivation and also to explore temporal patterns in social communication behaviour.

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To introduce the data…
To introduce the data… There are 261 numbers where the postcode provided by BT does not match the NSPDF There are 11,245 numbers which are not in the BT customer extract! They may be on BT customers logged for third party billing?
The NS-SEC 1 / low cars paradox suggests urban wealthy and students areas
The more NS-SEC 1, the more likely The more have 2+ cars, the less likely? The NS-SEC 1 / low cars paradox suggests urban wealthy and students areas Higher IMD employment deprivation - less likely - this score is a rate (% of population at risk) and has not been transformed so is comparable with census % in form Some regional effects
The more NS-SEC 1, the more likely The more have 2+ cars, the less likely? The NS-SEC 1 / low cars paradox suggests urban wealthy and students areas Higher IMD employment deprivation - less likely Some regional effects Model fit is poor so… random?
Calls to businesses - could be excluded using reciprocal rule but only have outgoing calls, could use destination hubs Residential numbers used as businesses - could be excluded by a rule on volume E.g. percentile cut Fraud - where numbers used as international forwarding for local calls, could be excluded on international call volumes?
Inspiration… the idea is that diversity/heterogeneity in communication networks should enable greater economic opportunity through ‘weak ties’ (Granovetter/Burt etc). Over-localised networks seen as limited
Same story dominance of local calls % local calls increases as deprivation increases
Same story dominance of local calls % local calls increases as deprivation increases
Same story dominance of local calls % local calls increases as deprivation increases
Same story dominance of local calls % local calls increases as deprivation increases
Instead of aggregating to LSOA level, this is household level Same story dominance of local calls % local calls increases as deprivation increases
Same story dominance of local calls % local calls increases as deprivation increases
Same story dominance of local calls % local calls increases as deprivation increases
Seen this before somewhere?
Same story dominance of local calls % local calls increases as deprivation increases
Same story dominance of local calls % local calls increases as deprivation increases
Same story dominance of local calls % local calls increases as deprivation increases
Same story dominance of local calls % local calls increases as deprivation increases

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