The document summarizes key findings from the 2010 Global Burden of Disease Study, which assessed the prevalence of 291 diseases and injuries and 67 risk factors across 187 countries from 1990 to 2010. It found that ischemic heart disease, HIV/AIDS, and lower respiratory infections caused the most disability-adjusted life years lost globally. The study applied consistent estimation methods to comprehensively measure the health burden and trends in both communicable and non-communicable diseases. It represents an improvement over previous analyses by strengthening statistical methods and expanding the scope of conditions and risk factors considered.

1. Biological & Medical
Informatics:!
the beginning
Daniel Himmelstein!
September 24, 2014
Hand Drawn Map of SF!
by Jenni Sparks
Before the Money Came!
Bettye LaVette

2. challenge
Hand Drawn Map of SF!
by Jenni Sparks

3. review article
The new engl and jour nal of medicine
Global Health
Measuring the Global Burden of Disease
Christopher J.L. Murray, M.D., D.Phil., and Alan D. Lopez, Ph.D.
From the Institute for Health Metrics and
Evaluation, University of Washington,
Seattle (C.J.L.M.); and the University of
Melbourne, School of Population and
Global Health, Carlton, VIC, Australia
(A.D.L.). Address reprint requests to Dr.
Murray at the Institute for Health Metrics
and Evaluation, 2301 Fifth Ave., Suite 600,
Seattle, WA 98121, or at cjlm@uw.edu.
N Engl J Med 2013;369:448-57.
DOI: 10.1056/NEJMra1201534
Copyright © 2013 Massachusetts Medical Society.
I
t is difficult to deliver effective and high-quality care to patients
without knowing their diagnoses; likewise, for health systems to be effective, it
is necessary to understand the key challenges in efforts to improve population
health and how these challenges are changing. Before the early 1990s, there was no
comprehensive and internally consistent source of information on the global bur-
den of diseases, injuries, and risk factors. To close this gap, the World Bank and the
World Health Organization launched the Global Burden of Disease (GBD) Study in
1991.1 Although assessments of selected diseases, injuries, and risk factors in se-
lected populations are published each year (e.g., the annual assessments of the
human immunodeficiency virus [HIV] epidemic2), the only comprehensive assess-
ments of the state of health in the world have been the various revisions of the GBD
Study for 1990, 1999–2002, and 2004.1,3-10 The advantage of the GBD approach is
that consistent methods are applied to critically appraise available information on
each condition, make this information comparable and systematic, estimate results
from countries with incomplete data, and report on the burden of disease with the
use of standardized metrics.
The most recent assessment of the global burden of disease is the 2010 study
(GBD 2010), which provides results for 1990, 2005, and 2010. Several hundred
investigators collaborated to report summary results for the world and 21 epidemio-
logic regions in December 2012.11-18 Regions based on levels of adult mortality,
child mortality, and geographic contiguity were defined. GBD 2010 addressed a
number of major limitations of previous analyses, including the need to strength-
en the statistical methods used for estimation.11 The list of causes of the disease
burden was broadened to cover 291 diseases and injuries. Data on 1160 sequelae
of these causes (e.g., diabetic retinopathy, diabetic neuropathy, amputations due
to diabetes, and chronic kidney disease due to diabetes) have been evaluated
separately. The mortality and burden attributable to 67 risk factors or clusters of
risk factors were also assessed.
GBD 2010, which provides critical information for guiding prevention efforts,
was based on data from 187 countries for the period from 1990 through 2010. It
includes a complete reassessment of the burden of disease for 1990 as well as an
estimation for 2005 and 2010 based on the same definitions and methods; this
facilitated meaningful comparisons of trends. The prevalence of coexisting condi-
tions was also estimated according to the year, age, sex, and country. Detailed results
from global and regional data have been published previously.11-18
The internal validity of the results is an important aspect of the GBD approach.
For example, demographic data on all-cause mortality according to the year, coun-
try, age, and sex were combined with data on cause-specific mortality to ensure
that the sum of the number of deaths due to each disease and injury equaled the
number of deaths from all causes. Similar internal-validity checks were used for
Global Burden of
Disease (2010)
Disease
Years Lost!
(million)
ischemic heart
disease
129.8
HIV-AIDS 81.5
Respiratory
Cancers
46.9
disability-adjusted life year (DALY) is a
measure of overall disease burden,
expressed as the number of years lost
due to ill-health, disability or early
death
DOI: 10.1056/NEJMra1201534Murray et al. NEJM. 2013
!
100 Million Pennies
http://www.kokogiak.com/megapenny

4. US Life Expectancy
Gregg Easterbrook (September 17, 2014) What
Happens When We All Live to 100?. The Atlantic
Calico:
500 Million USD

5. 1950 1960 1970 1980 1990 2000 2010
0.01.02.0
Increasing R&D Spending per New Drug Approval
SpendingperDrug*
● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●
●
● ● ●
●
● ●
● ● ● ●
●
●
● ●
●
● ●
●
●
●
●
●
●
●
●
●
●
●
●
● ●
●
●
●
●
●
●
●
●
●
●
●Td = 9.44
exponential model
1950 1960 1970 1980 1990 2000 2010
Year
log10(SpendingperDrug*)
−2−10
●
●
●
●
●
● ●
●
●
●
●
●
● ● ●
● ●
●
●
●
●
●
●
●
●
●
●
● ●
●
●
●
●
●
● ●
●
●
●
●
●
●
●
●
●
●
●
●
● ●
● ●
● ●
●
● ●
●
● ●
●
R2
= 0.95
linear model
confidence interval
prediction interval
*Spending in Billions of 2008 Dollars data from doi:10.1038/nrd3681
Himmelstein, Daniel; Baranzini, Sergio (2014): Increasing R&D Spending per New Drug Approval. figshare.
http://dx.doi.org/10.6084/m9.figshare.937004
1950 1960 1970 1980 1990 2000 2010
0.01.02.0
Increasing R&D Spending per New Drug Approval
SpendingperDrug*
● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●
●
● ● ●
●
● ●
● ● ● ●
●
●
● ●
●
● ●
●
●
●
●
●
●
●
●
●
●
●
●
● ●
●
●
●
●
●
●
●
●
●
●
●Td = 9.44
exponential model
1950 1960 1970 1980 1990 2000 2010
Year
log10(SpendingperDrug*)
−2−10
●
●
●
●
●
● ●
●
●
●
●
●
● ● ●
● ●
●
●
●
●
●
●
●
●
●
●
● ●
●
●
●
●
●
● ●
●
●
●
●
●
●
●
●
●
●
●
●
● ●
● ●
● ●
●
● ●
●
● ●
●
R2
= 0.95
linear model
confidence interval
prediction interval
*Spending in Billions of 2008 Dollars data from doi:10.1038/nrd3681

6. Physarum polycephalum:
Slime Mold
Plasmodium:!
• vegetative state!
• acellular!
• multinuclear!
• protoplasmic veins
(tubules)!
• locomotion by pulsation
- surface tension
http://youtu.be/MX2Fo4k6pxE
Signature habitat:!
shady, cool, moist

7. the present
Hand Drawn Map of SF!
by Jenni Sparks

8. The exponential rise of ‘omics’
Andrew Su
on Twitter
‘omics’ — collective characterization and
quantification of biomolecules

9. Data Scientist:
The Sexiest Job of the 21st Century
Meet the people who
can coax treasure out of
messy, unstructured data.
by Thomas H. Davenport
and D.J. Patil
hen Jonathan Goldman ar-
rived for work in June 2006
at LinkedIn, the business
networking site, the place still
felt like a start-up. The com-
pany had just under 8 million
accounts, and the number was
growing quickly as existing mem-
bers invited their friends and col-
leagues to join. But users weren’t
seeking out connections with the people who were already on the site
at the rate executives had expected. Something was apparently miss-
ing in the social experience. As one LinkedIn manager put it, “It was
Meet the people who
can coax treasure out of
messy, unstructured data
by Thomas H. Davenport
and D.J. Patil
70 Harvard Business Review October 2012
70 Harvard Business Review October 2012 Harvard Business Review October 201
Artwork: Tamar Cohen, Andrew J Buboltz, 2011
Definition (wikipedia):
!
the study of the generalizable
extraction of knowledge from data

10. comparison with maternal grandmother
The Dawn of Personalized Genomics
NHGRI GWAS Catalog

11. Open Source Explosion
Audio from:
Let’s Talk Bitcoin!
#134 Disruptive Leaps
Andreas Antonopoulos
& Jeffrey Tucker
Science graphic from:
http://nisd.net/academics/elementary-science

12. the past
Hand Drawn Map of SF!
by Jenni Sparks

13. • Aggregate microbial rDNA
content of a seawater sample
• richness of operational
taxonomic units (OTUs)
• species distribution modeling
Diversity of the Marine Metagenome
Ladau et al. (2013) ISME
doi:10.1038/ismej.2013.37
Katie Pollard
-180° -150° -120° -90° -60° -30° 0° 30° 60° 90° 120° 150° 180°
-180° -150° -120° -90° -60° -30° 0° 30° 60° 90° 120° 150° 180°
-90°
-60°
-30°
0°
30°
60°
90°
-90°
-60°
-30°
0°
30°
60°
90°
MICROBIS
FUHRMAN2008
POMMIER2007
GOS
Figure S1: Sampling locations for data used in constructing maps. Models with
zero to eight parameters were fitted using MICROBIS data. Predictive performance of
the models was evaluated using both internal measures of model performance (AIC, BIC,
and PRESS) and three independent data sets, collected at the locations shown in red,
green, and yellow (see Table S1). Analyses were based on 377 samples (234 MICROBIS,
30 GOS, 9 POMMIER2007, 103 FUHRMAN2008) collected from 164 distinct locations.
-180° -150° -120° -90° -60° -30° 0° 30° 60° 90° 120° 150° 1
-180° -150° -120° -90° -60° -30° 0° 30° 60° 90° 120° 150° 1
-90°
-60°
-30°
0°
30°
60°
90°
MICROBIS
FUHRMAN2008
POMMIER2007
GOS
Figure S1: Sampling locations for data used in constructing maps. Mode
zero to eight parameters were fitted using MICROBIS data. Predictive performa
the models was evaluated using both internal measures of model performance (AIC
and PRESS) and three independent data sets, collected at the locations shown
green, and yellow (see Table S1). Analyses were based on 377 samples (234 MICR
30 GOS, 9 POMMIER2007, 103 FUHRMAN2008) collected from 164 distinct loca

14. Diversity in June
Ladau et al. (2013) ISME
doi:10.1038/ismej.2013.372.05 2.20 2.35 2.50 2.65
Log10(OTU Richness)
-9
-6
-3
0
30
60
90
-9
-6
-3
0
30
Log10(OTU Richness)

15. Diversity in December
Ladau et al. (2013) ISME
doi:10.1038/ismej.2013.372.05 2.20 2.35 2.50 2.65
Log10(OTU Richness)
-9
-6
-3
0
30
60
90
-9
-6
-3
0
30

16. Slime Mold & the
Greater Tokyo
Rail System
Tero et al (2010) Science
DOI: 10.1126/science.1177894http://youtu.be/GwKuFREOgmo
• 17 cm (7 in) agar-
filled petri dish
• plasmodium for
Tokyo
• quaker oats for
cities
• vegetate for a day
• decentralized,
distributed
planning

17. Tero et al (2010) Science
DOI: 10.1126/science.1177894
aftermath: no illumination
aftermath: geographic
constraint using illumination

18. The SlimeNet was comparable or
preferable to the RealNet in terms of:!
• efficiency
• fault tolerance
• cost
Actual Rail Network Slime Tubule Network
Tero et al (2010) Science
DOI: 10.1126/science.1177894

19. Human Evolution & Population Genetics
John Novembre
Ryan Hernandez
• 3,192 Europeans
• 500,568 SNPs
• Reduced to 2d (PCA)
Veeramah & Hammer (2014) Nat Rev Genet
doi:10.1038/nrg3625
out-of-Africa
bottleneck
• Europeans have less
genetic diversity
than Africans
Novembre et al (2008) Nature
doi:10.1038/nature07331

20. Genes mirror geography within Europe
Novembre et al (2008) Nature
doi:10.1038/nature07331
• Despite the low diversity in Europeans, 500 thousand common
variants discriminate population diversity with high resolution.

21. Medical Informatics
- An invited segment by Antoine Lizée -
How to build
intelligence around
patient medical
records
Adriana Karembeu & Antoine Lizee
at Sandler Neurosciences Center, UCSF

22. 4500 visits - 600 patients – 10th year (UCSF EPIC STUDY)
Images ~200MB/visit
Brain MRI
T1, T2,
proton density
Processed MRI
Cortical Thickness,
Myelin
Overlays
CT, Myelin,
Anatomical labels
GWAS
500,000+ SNPs
HLA
A,B,C,
DRB1, DQB1
Patient data Age, sex, history, etc.
Clinical data
Clinical Scores, treatments
Patient reported
Quality of Life questionnaires
Processed data
MRI-based
ReferenceData
Genotypes
~1MB/patient
(Para-) Clinical Data
~250 variables/visit

23. Visits

26. hometown
kin
college
debate
camp
Guatemala
UCSF
research
Dartmouth
The Friendship Network of Daniel Himmelstein
Learn more online at:
http://dhimmel.com
• 1,278 nodes
• 40,255 edges

27. Highschool
Camp
College
Kin
UCSF
Research
Debate
1,278 nodes (1 type)
40,255 edges (1 type)
http://dhimmel.com
Facebook Friends
Genes
DiseasesPathophysiologies
Tissues
Genomic
Positions
Perturbations
Canonical
Pathways
BioCarta
KEGG
Reactome
miRNA
TFBS
Cancer
Hoods
Cancer
Modules
GO: BP
GO: MF
GO: CC
Oncogenic
Immunologic
Complex Diseases
29,241 nodes (19 types)
1,608,168 edges (20 types)
http://het.io

28. Multiple
SclerosisRF1 IL2RA
4 1 1 4
Multiple
SclerosisRF1 IRF8
4 1 1 4
Multiple
SclerosisRF1 CXCR4
4 2 1 4
Multiple
SclerosisRF1 Leukocyte
2 1 1 1
paths path
degree
product
degree
weighted
path count
0.707
0.25
0.25
0.177
0.677
0.707
ITCH
Lung
SUMO1
Multiple
Sclerosis
IRF1
Leukocyte
Crohn’s
Disease
IL2RA
IRF8
CXCR4
STAT3
expression
interaction
association
localization
association
association
association
interaction
Graph SubsetC
PDP(path) =
Y
d2Dpath
d w
metaedge-specific degrees
Network
G T De l
G G Di a
a aG D G Da
G Da
MetaPaths
GTDelGGDia
Multiple
SclerosisIRF1 IL2RA
4 1 1 4
Multiple
SclerosisIRF1 IRF8
4 1 1 4
Multiple
SclerosisIRF1 CXCR4
4 2 1 4
Multiple
SclerosisIRF1 Leukocyte
2 1 1 1
metapath paths path
degree
product
degree
weighted
path count
0.707
0.25
0.25
0.177
0.677
0.707
ITCHSUMO1
IL2RA
IRF8
CXCR4
interaction
a mG D P Dm
i iG G G Da
a lG D T Dl
e eG T G Da
physiology
B
D
PDP(path) =
Y
d2Dpath
d wm mG M G Dam mG M G Dam mG M G Dam mG M G Dam mG M G Dam mG M G Dam mG M G Da
m mG M G Dam mG M G Dam mG M G Dam mG M G Dam mG M G Dam mG M G Dam mG M G Da
DWPC(metapath) =
X
path2P aths
PDP(path)
metaedge-specific degrees
Feature Computation
{Cancer Hood}
{Positional}
GeTeGaDGiGeTlDGeTlD
{GO
Function}
{GO
Component}
{miRNA Target}
{BioCarta}
{Oncogenic}
{TF Target}
GaD
(any gene)
{Cancer Module}
GiGiGaD
{GO
Process}GiGaD{KEGG}
{Immunologic}
{Reactome}
{Perturbation}
GaDmPmD
GaD
(any disease)
GaDlTlD
GaDaGaD
2 0 2 4
Standardized Coe cient
Method (AUROC)
ridge (0.829)
lasso (0.823)
Machine Learning
0.0
0.2
0.4
0.6
0.8
1.0
0.0 0.2 0.4 0.6 0.8 1.0
False Positive Rate
Recall
Partition (AUROC)
Testing (0.829)
Training (0.810)
Performance
0.2
0.6
1.0
1.4
1.8
Meta2.5
0.0 0.2 0.4 0.6 0.8 1.0
P-value
Density
Combine Predictions
& Statistical Evidence
15
Gene Meta2.5 HNLP WTCCC2
JAK2 0.047 0.102 0.0015
REL 0.001 0.040 0.0003
SH2B3 0.012 0.034 0.0130
RUNX3 0.016 0.025 0.0073
Table 5. Multiple sclerosis gene discovery.
Discover Novel
Susceptibility Genes

29. Interactive Web Browser - http://het.io

30. Mechanisms of Pathogenesis
Gene—{MSigDB Collection}—Gene—Disease DWPC Model
—
—
—
———
—
—
——
— — —
—
—
—
—
———
—
—
——
— — —
—
— —
—
—
0.4
0.6
0.8
1.0
Positional
C
ancerH
oodBioC
arta
G
O
C
om
ponent
m
iR
N
A
Target
G
O
FunctionR
eactom
eO
ncogenicTF
Target
KEG
G
G
O
Process
C
ancerM
odule
Im
m
unologic
Perturbation
Lasso
R
idge
AUROC
— ——
— ——
— ——
—
—— ——
— —
—
—
—
—
— —
—
—
0.4
0.6
0.8G
iG
aDG
eTeG
aD
G
eTlD
G
iG
eTlDG
aD
aG
aDG
aD
m
Pm
D
G
iG
iG
aD
G
aD
lTlD
G
aD
(any
gene)
G
aD
(any
disease)
Lasso
R
idge
AUROC
Pathophysiology
degenerative
immunologic
metabolic
neoplastic
psychiatric
unspeci c

31. c
c

32. c
c
Sergio!
Baranzini
Ryan
Hernandez
John
Witte
Andrej
Sali
Katie
Pollard
Patsy
Babbitt

33. Decreased Lung Cancer at High Elevations
●
●
●●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●●
●
●
●
β = −9.167
R2
= 0.202
25
50
75
100
0 1 2
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
● ●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
● ●
●
●
β = −7.234
R2
= 0.252
−40
−20
0
20
40
−1 0 1
A B
Elevation (km) Residual Elevation
LungCancerIncidence
LungCancerIncidenceResidual
Bivariate Plot
●
●
●●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●●
●
●
●
β = −9.167
R2
= 0.202
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
β = −7.781
R2
= 0.109
25
50
75
100
120
160
0 1 2
)
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
● ●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
● ●
●
●
β = −7.234
R2
= 0.252
●
●
●
●● ●●
●● ●
●
●
●
●
● ●
●
● ●
●
●
●●
●
●
●
● ●
β = −4.056
R2
= 0.04
−40
−20
0
20
40
25
−1 0 1
A B
Elevation (km) Residual Elevation
LungCancerIncidence
LungCancerIncidenceResidual
Partial Regression Plot
• Counties of the American West
• Lung cancer versus elevation
• Publicly-available data

34. Lung Breast Colorectal Prostate
-0.6
-0.4
-0.2
0.0
0.2
0.4
2 5 8 2 5 8 2 5 8 2 5 8
Subset Size
StandardizedElevationCoecient
500
600
700
Model BIC
Association specific to lung cancer
Kamen
Simeonov
• Inhaled carcinogen
• Oxygen concentration decreases by ~11%
for every 1000 meter rise in elevation
Lung Breast Colorectal Prostate
-0.6
-0.4
-0.2
0.0
0.2
0.4
2 5 8 2 5 8 2 5 8 2 5 8
Subset Size
StandardizedElevationCoecient
500
600
700
Model BIC
Lung Breast Colorectal Prostate
-0.6
-0.4
-0.2
0.0
0.2
0.4
2 5 8 2 5 8 2 5 8 2 5 8
Subset Size
StandardizedElevationCoecient
500
600
700
Model BIC

35. the future
Hand Drawn Map of SF!
by Jenni Sparks

36. Subscription Publishing
Health science journal subscription
costs are skyrocketing
© Association of Research Libraries, 2013
$0
$2,000
$4,000
$6,000
$8,000
$10,000
$12,000
$14,000
$16,000
$18,000
$20,000
1.50%
1.70%
1.90%
2.10%
2.30%
2.50%
2.70%
2.90%
3.10%
3.30%
3.50%
3.70%
1982
1983
1984
1985
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
Intenthousands(multiplyvaluesby100000)
Library Expenditure as % of Total University Expenditure (Average of Select US ARL Libraries)
Total University Expenditure (Average of Select US ARL Libraries)
Library and University Expenditure
Trends (Time-Series)
Library and University Expenditure
Trends (Time-Series)
Library and University Expenditure
Trends (Time-Series)
Library and University Expenditure
Trends (Time-Series)
Library and University Expenditure
Trends (Time-Series)
Library and University Expenditure
Trends (Time-Series)
Library and University Expenditure
Trends (Time-Series)
Library and University Expenditure
Trends (Time-Series)
1982 2011
1.7%
3.7%
year
%ofuniversitybudgets
forlibraries
Library budges are nosediving
http://www.library.ucsf.edu/services/scholpub/journalcosts
• Libraries are canceling subscriptions
• Research is paywalled, inaccessible
to those who could benefit
• Scientists desire their findings to be
widely-applied
• Research funding is public
• Very small percentage of individuals
have institutional access
• Academia doesn’t succeed in a
vacuum — innovation grows from
diverse and plentiful inputs Audio from:
Let’s Talk Bitcoin!
#134 Disruptive Leaps
Andreas Antonopoulos

37. Per Article Cost
from "Open Access: Market Size, Share, Forecast, and Trends"
Outsell. January 31, 2013
!
Subscription: $4,000.00
Open Access: $950.00
UCSF Open Access Fund
http://www.library.ucsf.edu/services/scholpub/oa/fund/eligibility
Fully OA Journal: $2,000
Hybrid OA: $1,000
• PeerJ — Lifetime publishing
plan for $99
• eLife — currently no APC,
“pain free publication”
• PLOS, BMC, Specialty Pubs
• F1000 Research, pre-review
publication
• preprints, arRxiv & bioRxiv

38. Article-level metrics
doi:10.1371/journal.pone.0013636.g005
Open Access increases Citations
Gargouri et al. PLOS One. 2010
• Alternative to journal impact
factor
• Citations, downloads, views,
social media
• Accelerates science —
impact factor = rejection
• Expands the audience
evaluating article importance
and quality
• Already used: h-index
Grow in importance

39. Public Data increases Citations
citations
Piwowar & Vision (2013)
DOI: 10.7717/peerj.175
• 10,555 microarray
studies
• Classified studies
by data availability
• 8 categories of
covariates

40. Availability & Reuse
• only applies to
original research
articles
• journals often
withhold the typeset
version
• does not affect reuse
Creative Commons Attribution Alone
Mandatory Archiving
!
NIH: PubMed Central
UC: eScholarship
• subscription journal
require the transfer of
article ownership
• enforce the article
copyright
• require licensing for
reuse

41. Tools for Efficiency & Reproducibility
Version control:
Online code repositories:
Interactive programming
environments:
ipython notebook

42. Personal Website
Clint Cario
clintcario.com
Daniel Himmelstein
dhimmel.com
Brian O’Donovan
iambrianodonovan.com
Kieran Mace
mace.co
Andrew Sczesnak
andrewsczesnak.com
Kyle Barlow
kylebarlow.com

43. your
beginning
Hand Drawn Map of SF!
by Jenni Sparks