describing & understanding neighborhood characteristics through online social media
TRANSCRIPT
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(CC BY 2.0) Rachel Elaine, flic.kr/p/fw8HSn
Describing & Understanding Neighborhood Characteristics through Online Social MediaMohamed Kafsi Henriette CramerBart Thomee, David A. Shamma
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CC Steve, flic.kr/p/5tm2Ci
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What’s unique about Florence? What’s similar to other cities?
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CC Pedro, flic.kr/p/85QqvB
CC Mitch Altman, flic.kr/p/ffHDeY
Lisboa
Paris
in Florence?
Which neighborhoods have characteristic street art?
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masses of geotagged, user community-generated content
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User-generated content *finding, describing, defining locales & boundaries (e.g. ZoneTag, TagMaps ‘07, Livehoods'12, Thomee & Rae’13 etc.)
CC Momo, flic.kr/p/6bKMtX
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But #1: locally frequent is not always specifically descriptive blackandwhite, gotham, tribeca
Brett Weinstein, flic.kr/p/RHWan
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Bert Kaufmann, flic.kr/p/dEfd12
#2 regions within regions Is ‘desert’ a descriptor of Las Vegas?or rather the surrounding area?
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Problem: let’s describe neighborhoods with flickr content go beyond local, compare neighborhoods
focus on user-defined noisy tags -ignore the pixels-
build model to distinguish ‘specifically descriptive’ local content work with the geo-hierarchies that people are familiar with compare with human reasoning (interviews & survey)
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Goal: -Find specific descriptions of pre-defined regions -Quantify their uniqueness -Map similar regions
Idea: -Define any geographical hierarchy of regions -Quantify the descriptiveness of tags with respect to a given geographical level
Geographical hierarchy of tags
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Country
Cou
ntry
City A City B City C
Citi
es
Hood B.1
Hood B.2
Hoo
d
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USA
Cou
ntry
Chicago
SF New York
Citi
es
Mission SOMA
Hoo
d
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Country
Cou
ntry
City A City B City C
Citi
es
Hood B.1
NeighborhoodB.2H
ood
Randomly sample tags from the nodes along the path from leaf to root
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+
Distribution of tags in neighborhood n
θcountry(n)
πcountry(n)
θn
πn
θcity(n)
πcity(n)
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(cc by 2.0) S J Pinkney, flic.kr/p/8cNAgd (CC BY 2.0) !STORAX, flic.kr/p/39Wstq
SF & Manhattan
- Sample of 8M geo-tagged photos - 20M tags, - vocabulary of 8000 unique tags
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Probability of tag t in neighborhood n
p(t|n) =X
v2Rn
✓v(t) p(z = d(v) | n)
depth of node v
path from the leaf n to the root of the geo-tree multinomial distribution associated with node v
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Training
- Expectation-Maximization to learn the model’s parameters
- Fast convergence
- Scales. Worst case running time O(N V D)
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Results: assigning tags to a level
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Mission, SF
California Mission SF USA Graffiti Art Mural Valencia Food Car
David McSpadden, flic.kr/p/oVLorr
Most frequent tags
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Country: 0.06 City: 0.33Neighborhood: 0.61
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California Mission SF USA Graffiti Art Mural Valencia Food Car
Mission, SFMost frequent tags
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Country: 0.06 City: 0.33Neighborhood: 0.61
David McSpadden, flic.kr/p/oVLorr
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New York Manhattan USA Midtown Skyscraper Time square Light Moma Broadway Rockfeller
Midtown South, Manhattan
CC Jeffrey Zeldman, flic.kr/p/s1eE5W
Most frequent tags
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Country: 0.21City: 0.32Neighborhood: 0.47
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New York Manhattan USA Midtown Skyscraper Time square Light Moma Broadway Rockfeller
Midtown South, ManhattanMost frequent tags
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Country: 0.21City: 0.32Neighborhood: 0.47
CC Jeffrey Zeldman, flic.kr/p/s1eE5W
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1. Where do I find tag t? (i.e. where is tag t most locally descriptive)
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Food
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Hipster
Mapping Tags in San Francisco
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2. where are the unique ‘hoods?quantifying uniqueness
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Uniqueness
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3. What’s the east-village of SF? mapping & comparing neighborhoods
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San Francisco Manhattan Top common tags
Mission East Village (0.23) graffiti, food, restaurant, mural, bar
Golden gate park Washington heights (0.26)Upper west side (0.22)
park, museum, nature, flower, bird
Financial district Battery park (0.29) Midtown Manhattan (0.27)
downtown, building, skyscraper, city, street
Chinatown Chinatown (0.85)Chinatown, Chinese, downtown, dragons, lantern
Castro West village (0.06) park, gay, halloween, pride, bar
sim(n, n0) =
PVt=1 ✓n(t) ✓n0(t)qPV
t=1 ✓n(t)2
qPVt=1 ✓n0(t)2
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Does it match human reasoning?
presentation -or-
model adaptation
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locals’ reasoning in classifying tags’ local specificity
10 interviews
Survey: 22 human classifiers
3 neighborhoods: classify 32 tags 1291 tag classifications
########
########
###### s
an-franc
isco, Ca
stro/Upp
er Marke
t ######
########
########
sfmoma
sf
embarc
adero
baseb
all
san
mission
market
tram
rainbow
muni
pride
city
sanfranc
isco
gay
califo
rnia
alcat
raz
streetca
r
francis
co
male
dolor
espark
castro
soma
church
sign
street
theater
flag
usa
dolores
night
movie
hallo
ween
########
########
########
#### san
-francis
co, Mari
na #####
########
########
########
boat
sanfran
cisco
flower
s
bird
water
califor
nia
usa
night
francisc
o
embarca
dero
street
bridg
e
alcatraz
presidi
o
pond
explo
ratorium
sf
columns
mason
fort
gate
swan
golden
bay
city
sign
san
finan
cial
sailing
palace
marina
sfmom
a
roomfars
outh-lm:
scripts
kafsi$
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Principle 1. While users’ tagging is personal, varied, local;
(e.g. Naaman et al., 07)
- the community generates similar meta-content
(e.g. Rost et al., 2013)
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2. Personal experiences shape classifications
this slides features 3 different churches.
CC Justin Pickard, flic.kr/p/6hWpoa
CC torbakhopper, flic.kr/p/9PYSjx
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(CC BY 2.0) Dustin Gaffke, flic.kr/p/aCvuxP
No human ground truth.
SF: North Beach?
P1: ’party land!’
P2: ‘I don’t believe there’s much of a nightlife there’
3. Teaching opportunity? ‘mistakes’ or perspectives?
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Proposed geo-model extensions (i.e. humans aren’t wrong)
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1. even when using existing regions, detecting sub-regions is important
this is (not) North Beach
(CC BY 2.0) David Ohmer, flic.kr/p/2xT9rU
Most ‘rejected’ by our human classifiers:
Fisherman’s Wharf in North Beach in our official neighborhood dataset… but it’s its own thing.
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San Francisco neighborhood map (1960) From The Urban Aesthetic: Evolution of a Survey System.
(CC BY 2.0) Eric Fischer, flic.kr/p/dTxhBg
Night, The Mission:
“before…you wouldn’t be caught there at night-time… 20 years ago it was a different neighborhood.”
2. boundaries, character, data change over time
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(CC BY 2.0) Σταύρος, flic.kr/p/6ZZX6A
3. Topography & closeness matters: make neighborhood boundaries permeable
Geographic Hierarchical model with Adjacency, extension.
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(CC BY 2.0) Rachel Elaine, flic.kr/p/fw8HSn
Mohamed Kafsi - [email protected]
Henriette Cramer@hsmcramer [email protected]
Bart [email protected]
David A. [email protected]
SUMMARIZING: Geographical Hierarchy Model - resilient & scalable probabilistic model (with adjacency extension) - applied on sample Flickr photos SF & NY - we can describe & compare neighborhoods, quantify uniqueness & similarity - mind the individual human interpretation gap: we present an aggregate view based on community-generated content.
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