=Paper=
{{Paper
|id=Vol-1644/keynote3
|storemode=property
|title=None
|pdfUrl=https://ceur-ws.org/Vol-1644/keynote3.pdf
|volume=Vol-1644
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==None==
<pdf width="1500px">https://ceur-ws.org/Vol-1644/keynote3.pdf</pdf>
<pre>
            Exploring Big Urban Data

                             Juliana Freire

                         New York University
                       juliana.freire@nyu.edu



Abstract. Today, 50% of the world’s population lives in cities and the
number will grow to 70% by 2050. Cities are the loci of economic activity
and the source of innovative solutions to 21st century challenges. At the
same time, cities are also the cause of looming sustainability problems in
transportation, resource consumption, housing affordability, and inade-
quate or aging infrastructure. The large volumes of urban data, along
with vastly increased computing power and improved user interfaces en-
able analysts to better understand cities. Encouraging success stories
show better operations, more informed planning, improved policies, and
a better quality of life for residents. However, analyzing urban data of-
ten requires a staggering amount of work, from identifying relevant data
sets, cleaning and integrating them, to performing exploratory analyses
over complex, spatio-temporal data. Our long-term goal is to enable in-
terdisciplinary teams to crack the code of cities by freely exploring the
vast amounts of data cities generate. This talk describes challenges which
have led us to fruitful research on data management, data analysis, and
visualization techniques. I will present methods and systems we have
developed to increase the level of interactivity, scalability, and usabil-
ity for spatio-temporal analyses. This work was supported in part by
the National Science Foundation, a Google Faculty Research award, the
Moore-Sloan Data Science Environment at NYU, IBM Faculty Awards,
NYU School of Engineering and Center for Urban Science and Progress.

</pre>