We conduct our research together with multiple academic and industrial partners. As innovation often comes from synergy we are always looking for new collaborators.
Our most recent projects:
|Impact of Urban Deployments||Under support of Future Cities Catapult we performed assessment of economic, social and environmental impacts of urban technology deployments, such as CitiBike, UberPOOL, LinkNYC, Midtown-In-Motion etc. using multiple sources of big urban data on human mobility, commercial activity etc|
|The Formulae Of Social Resilience||We leveraged social media data for defining urban communities in NYC and quantified their susceptibility to possible malicious social influences. The formulae of social resilience has been discovered – a set of socio-economic and network characteristics making community more resilient/susceptible for such influences|
|Disruptive synergy of NYC subway||We quantify delays experienced by subway passengers arising from disruptive events,particularly from those that occur simultaneously. We determine if and to what extent the effect under such simultaneous scenarios is greater than it would be had the same disruptions occurred as separate events. The findings can be useful in supporting the planning decisions necessary to prepare for the harmful consequences arising from disruptions to complexurban networks.|
|Signature Of Urban Concerns||We leverage the structure of 311 service requests to build a unique signature of the local urban context, thus being able to serve as a low-cost ol for urban stakeholders. Considering examples of New York City, Boston and Chicago, we demonstrate how 311 Service Requests recorded and categorized by type in each neighborhood can be utilized to generate a meaningful classification of locations across the city, based on distinctive socioeconomic profiles. Moreover, the 311-based classification of urban neighborhoods can present sufficient information to model various socioeconomic features. Finally, we show that these characteristics are capable of predicting future trends in comparative local real estate prices. We demonstrate 311 Service Requests data can be used to monitor and predict socioeconomic performance of urban neighborhoods, allowing urban stakeholders to quantify the impacts of their interventions.|