What is (and isn’t) a smart city? Through a research assessing the economic feasibility of a new incentive-based rezoning Tel-Aviv city center, analyzed on the parcel level using GIS data and Python code, I am trying to depict the way for architects and planners to get more power in the shape of the cities of tomorrow, and to leverage our planning skills with big data
1. Smart City? Smart Planning
Big Data for better and truly smart cities
* A data-driven analysis of Tel-Aviv city’s rezoning feasibility
Dana Chermesh, Jan 2019
danachermesh@gmail.com
github.com/danachermesh
2. * Why worry about cities?
Urban areas only occupy ~1% of the Earth’s land,
yet they generate 80% of global GDP, emit 70% of
greenhouse gasses, and hold 54% of the world’s
population.
Cities are the world
3. Cities are the world
Urban areas only occupy ~1% of the Earth’s land,
yet they generate 80% of global GDP, emit 70% of
greenhouse gasses, and hold 54% of the world’s
population.
* Why worry about cities?
By the year 2050, 75% of humanity is
expected to dwell in cities. How smart
will those cities be? How will life look
like in them and who will shape them?
4. 21st century cities’ challenges
cities Already deal with the current and future challenges of the 21st
century and the largest and most rapid urbanization process
‣ Overpopulation
‣ Housing
‣ Mobility
‣ Infrastructure
‣ Health
‣ Safety + Security
‣ Energy
‣ Water
‣ Waste
‣ Resilience
‣ Pollution
‣ Poverty
5. Cities are the future
Cities are the solutions to their own challenges
Richard Florida, CityLab, Oct 2018
Richard Florida, CityLab, July 2018
6. Cities are the future
Richard Florida, CityLab, Sep 2018
Ian Klaus, CityLab, Dec 2018
Cities are the solutions to their own challenges
7. Cities are the future
ClimateMayors.org, June 2017
Richard Florida, CityLab, Mar 2017
Cities are the solutions to their own challenges
8. Cities are the future
Ian Chipman, Stanford Engineering, Jan 2017
Josh Kron, The Atlantic, Nov 2012
ClimateChangeFork, Dec 2016
Cities are the solutions to their own challenges
9. Cities are the future
World Economic Forum, July 2017
International Water Association, Apr 2015
Cities are the solutions to their own challenges
27. ** Dana Chermesh; Principles of Urban Informatics (PUI) class, NYU CUSP, Fall 2017
Figure: linkNYC locations on top
of a choropleth of broadband
access from total households.
The linkNYC stations are shown on top
of % of broadband access from total
households.. The linkNYC stations are
colored by the date they were installed
on; darker red is older station.
% of households
with WiFi from
total households
the case of LinkNYC // Sidewalk Labs
Smart city for whom?
28. ** Dana Chermesh; Principles of Urban Informatics (PUI) class, NYU CUSP, Fall 2017
MIDTOWN MANHATTAN
Figure: linkNYC locations on top
of a choropleth of broadband
access from total households.
The linkNYC stations are shown on top
of % of broadband access from total
households.. The linkNYC stations are
colored by the date they were installed
on; darker red is older station.
% of households
with WiFi from
total households
the case of LinkNYC // Sidewalk Labs
Smart city for whom?
29. Ava Kofman, The Intercept, Sep 2018
the case of LinkNYC // Sidewalk Labs
Smart city for whom?
42. Sponsor: Local Initiatives Support Corporation (LISC NYC)
CUSP Mentor: Prof. Neil Kleiman; UCB Mentor: Prof. Karen Chapple
Team: Ruben Hambardzumyan, Hao Xi, Gerardo Rodriguez, Dana Chermesh
Urban Displacement Project for
the Greater New York
47. CHECK OUT our UDPNY Website + Mapping tools @ udpny.org
Mobility Opportunities Explorer
Where can the displaced move to?
48. Where can they go?
Stamford, CT
Bushwick, BK
Chelsea, MN
East Bronx, BX
Ongoing Gentrification
Advanced Gentrification
UDPNY Website + Mapping
tools @ udpny.org
49. Do the classic planning processes
succeed to benefit from the use and
applications of big data?
Smarter City Planning?
*
What makes a city Smart ?
PlanningOperations
56. The White City of Tel Aviv – The
Modern Movement
has been inscribed upon the World
Heritage List of the convention for the
protection of the World Cultural and
Natural Heritage. Inscription on this
List confirms its outstanding universal
value, which deserves protection for
the sake of humanity.
Tel Aviv was founded in 1909 and
developed under the British Mandate
(1920-1948). The White City was
constructed from the early 1930 till
1948, based on the urban plan by Sir
Patrick Geddes’ reflecting modern
organic planning principles. The
buildings were designed by Jewish
architects who were trained in Europe
where they practiced their profession
before emigrating to Palestine-Eretz
Yisrael. They created an outstanding
architectural ensemble of the modern
movement in a new cultural context.
أﳊﺮﻛﺔ – اﻟﺒﻴﻀﺎء أﺑﻴﺐ -ﺗﻞ ﻣﺪﻳﻨﺔ
.اﳊﺪﻳﺜﺔ
اﻟﻌﺎﳌﻲ اﻟﺘﺮاث ﻗﺎﺋﻤﺔ ﻓﻲ ﻋﻨﻬﺎ اﻹﻋﻼن ﰎ
ﻟﻠﺜﻘﺎﻓﺔ اﻟﻌﺎﳌﻲ اﻟﺘﺮاث ﺣﻤﺎﻳﺔ ﻣﻌﺎﻫﺪة ﺑﺤﺴﺐ
.ﻟﻠﻌﺎم واﻟﻄﺒﻴﻌﺔ
ﻋﻦ ﻳﻌﺒﺮ اﻟﻘﺎﺋﻤﺔ ﻫﺬه ﻓﻲ ﺗﻞ-أﺑﻴﺐ ﺷﻤﻞ إن
ﻟﻠﻤﺪﻳﻨﺔ ةﺪﻳﺮﻔﻟا ﻦﻣ ﺎﻬﻋﻮﻧ اﻟﻌﺎﳌﻴﺔ اﻟﻘﻴﻤﺔ
ﻟﺼﺎﻟﺢ ﻋﻠﻴﻬﺎ اﳊﻔﺎظ ﺗﻠﺰم اﻟﺘﻲ اﻟﺒﻴﻀﺎء
.اﻟﻘﺎدﻣﺔ اﻷﺟﻴﺎل ﻓﻲ اﻹﻧﺴﺎﻧﻴﺔ
ازدﻫﺮت ﺣﻴﺚ ، ١٩٠٩ اﻟﻌﺎم ﻓﻲ ﺗﻞ-أﺑﻴﺐ أﻗﻴﻤﺖ
ﺑﲔ اﻟﺒﺮﻳﻄﺎﻧﻲ اﻻﻧﺘﺪاب ﺣﻘﺒﺔ ﻓﻲ وﺗﻄﻮرت
ﻓﻲ اﻟﺒﺪء ﰎ وﻗﺪ ، (١٩٢٠-١٩٤٨) اﻷﻋﻮام
وﺣﺘﻰ اﻟﺜﻼﺛﻴﻨﺎت ﻓﻲ اﻟﺒﻴﻀﺎء اﳌﺪﻳﻨﺔ ﺑﻨﺎء
ﺣﺴﺐ وذﻟﻚ ، ﻣﺪﻧﻲ أﺳﺎس ﻋﻠﻰ ١٩٤٨ اﻟﻌﺎم
رواد ﻣﻦ وﻫﻮ ﻏﺪس ﺑﺘﺮﻳﻚ اﻟﺴﻴﺮ ﺗﺨﻄﻴﻂ
اﻟﻘﺮن ﻓﻲ اﳊﺪﻳﺚ اﳌﺪﻧﻲ اﻟﺘﺨﻄﻴﻂ ﻣﺒﺎدئ
اﳌﺪﻳﻨﺔ ﺑﻴﻮت ﺗﺨﻄﻴﻂ ﺑﺈﻋﺪاد ﻗﺎم .اﻟﻌﺸﺮﻳﻦ
ﻳﻬﻮد ﻣﻌﻤﺎرﻳﲔ ﻣﻬﻨﺪﺳﲔ ﻣﻦ ﻣﺠﻤﻮﻋﺔ
ﻣﻬﻨﺘﻬﻢ ﻓﻲ واﺧﺘﺼﻮا وﺗﺮﺑﻮا ﻧﺸﺆوا ﻗﺪ ﻛﺎﻧﻮا
أرض – ﻓﻠﺴﻄﲔ إﻟﻰ ﻫﺠﺮﺗﻬﻢ ﻗﺒﻞ أوروﺑﺎ ﻓﻲ
ﳑﻴﺰا ﻣﻌﻤﺎرﻳﺎ ﺗﻜﺎﻣﻼ أوﺟﺪوا ﺣﻴﺚ ،إﺳﺮاﺋﻴﻞ
ﺟﻮ ﻓﻲ اﳊﺪﻳﺜﺔ اﻟﻌﺼﺮ وﻟﻐﺔ ﻳﺘﻼءم وﻣﺤﻠﻴﺎ
.ﺟﺪﻳﺪ ﺣﻀﺎري
–
.
.
-
.
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TAU, 20131
#delirious_tlv
Tel-Aviv’s
Preservation Plan
(2008)
Preservation
level A
Preservation
level B
70. Borough 3 Borough 4
The New NorthThe Old North
Jaffa
(Borough 7)
Borough 5
1. HISTORY Boroughs 1+2
<
Borough 6
Tel-Aviv
City Center
Mediterranean
Sea
74. “The White City” zone
c
1. HISTORY
“The White City of Tel Aviv is a synthesis of outstanding significance
of the various trends of the Modern Movement in architecture and
town planning in the early part of the 20th century. Such influences
were adapted to the cultural and climatic conditions of the place, as
well as being integrated with local traditions”
2003
76. ‣ Approved: December 2017
‣ One out of four renewal plans being prepared for Tel-Aviv city
center boroughs (3,4,5,6) and the first one to be approved
‣
Plan TA/3616/A Instructions,
Israeli Planning Administrations
2. Plan TA/3616/A
77. ‣ Act as a lever for urban renewal, by upgrading non-historical
buildings and increasing the housing stock of the city center
‣ Make buildings more resilient to earthquakes according to
national masterplan no.38
‣ Preserving the spatial qualities of the unique city center’s
urban scape, including following UNESCO’s requirements
Not eligible for:
‣ Historical Buildings
‣ New Buildings (>1980)
‣ Non-residential Buildings
*
Plan’s Objectives
2. Plan TA/3616/A
Plan TA/3616/A Instructions,
Israeli Planning Administrations
78. The plan is Incentive-based
*
$
(The developer burdens all the costs
of a project, including giving back to
homeowners their existing floor area)
> HOW?
2. Plan TA/3616/A
Plan TA/3616/A Instructions,
Israeli Planning Administrations
79. == M2
2. Plan TA/3616/A
> HOW?
Plan TA/3616/A Instructions,
Israeli Planning Administrations
The plan is Incentive-based
*
$The Incentive is Floor-Area
80. 2. Plan TA/3616/A
> HOW?
Plan TA/3616/A Instructions,
Israeli Planning Administrations
== M2
The plan is Incentive-based
*
$The Incentive is Floor-Area
Parameters for calculating
“development rights”
‣ Setbacks (Meters)
‣ Floors (#)
‣ Density coefficient (housing units)
*
81. Building Footprint
2. Plan TA/3616/A
> HOW?
Plan TA/3616/A Instructions,
Israeli Planning Administrations
Parameters for calculating
“development rights”
‣ Setbacks (Meters)
‣ Floors (#)
‣ Density coefficient (housing units)
*
== M2
The plan is Incentive-based
*
$The Incentive is Floor-Area
82. Floor Area
2. Plan TA/3616/A
> HOW?
Plan TA/3616/A Instructions,
Israeli Planning Administrations
== M2
The plan is Incentive-based
*
$The Incentive is Floor-Area
Parameters for calculating
“development rights”
‣ Setbacks (Meters)
‣ Floors (#)
‣ Density coefficient (housing units)
*M2
83. 2. Plan TA/3616/A
> HOW?
Plan TA/3616/A Instructions,
Israeli Planning Administrations
M2
Floor Area Ratio (FAR)
*
M2
84. 1
Building Footprint
M2
Floor Area
%
23
2. Plan TA/3616/A
> HOW?
Plan TA/3616/A Instructions,
Israeli Planning Administrations
Floor Area Ratio (FAR)
(lot coverage)(lot coverage X floors)
85. The plan differs between:
‣ New Construction / Building Extensions
‣ within / outside White City zone
‣ Parcel’s area <> 500 m2 (UNESCO)
‣ Residential / Commercial street
<**
** This research analyzes new construction possibility only
2. Plan TA/3616/A
Plan TA/3616/A Instructions,
Israeli Planning Administrations
Parameters:
‣ Setbacks
‣ no. of floors
‣ Density coefficient
*
Principles:
86. The plan differs between:
‣ New Construction / Building Extensions
‣ within / outside White City zone
‣ Parcel’s area <> 500 m2 (UNESCO)
‣ Residential / Commercial street
<
2. Plan TA/3616/A
Plan TA/3616/A Instructions,
Israeli Planning Administrations
Parameters:
‣ Setbacks
‣ no. of floors
‣ Density coefficient
*
Principles:
87. The plan differs between:
‣ New Construction / Building Extensions
‣ within / outside White City zone
‣ Parcel’s area <> 500 m2 (UNESCO)
‣ Residential / Commercial street
<
2. Plan TA/3616/A
Plan TA/3616/A Instructions,
Israeli Planning Administrations
Parameters:
‣ Setbacks
‣ no. of floors
‣ Density coefficient
*
Principles:
88. 1+
Parameters:
‣ Setbacks
‣ no. of floors
‣ Density coefficient
*
2. Plan TA/3616/A
Plan TA/3616/A Instructions,
Israeli Planning Administrations
The plan differs between:
‣ New Construction / Building Extensions
‣ within / outside White City zone
‣ Parcel’s area <> 500 m2 (UNESCO)
‣ Residential / Commercial street
Principles:
89. total addition of 8,000 housing units is expected
According to the city, the plan can create 16,000 new
housing units, with an assumption of 50% utilization >>
* Today there are 34,000 housing units in borough 3
2. Plan TA/3616/A
Plan TA/3616/A Instructions,
Israeli Planning Administrations
Housing Units
^ Additional^ Existing
91. 3. DATA &
METHODOLOGY
*Analysis assumption:
Equal and high
demand for land
For all of Borough 3 area
Borough 3
The Old North
92. 3. DATA &
METHODOLOGY
Writing a python code that reads real
gis data of every building and parcel
in the borough, calculates it according
to the rezoning’s instructions to
assess the allowed floor area to be
built. This to determine whether the
incentive per building is big enough to
assume it will be utilized
93. 3. DATA &
METHODOLOGY
gis data
‣ Address
‣ Parcel Size (m2)
‣ Footprint (m2)
‣ Building Type
‣ no. of Floors
‣ Historical Buildings
‣ Boundaries: Borough 3,
White City Zone
>> INPUT
< Python code >
<<
Writing a python code that reads real
gis data of every building and parcel
in the borough, calculates it according
to the rezoning’s instructions to
assess the allowed floor area to be
built. This to determine whether the
incentive per building is big enough to
assume it will be utilized
105. My final input dataset
3.2 reading, cleaning, munging data
< 2,161 Parcels >
(out of 4,560 parcels in borough 3)
106. gis data
1
M2
Floor Area
2
Floor Area Ratio (FAR)
>> INPUT
CALcULATED
(EXISTING)
(EXISTING)
< Python code >
<<<<
3. DATA &
METHODOLOGY
‣ Address
‣ Parcel Size (m2)
‣ Footprint (m2)
‣ Building Type
‣ no. of Floors
‣ Historical Buildings
‣ Boundaries: Borough 3,
White City Zone
107. gis data
1
M2
Floor Area
2
>> INPUT
CALcULATED
(EXISTING)
(EXISTING)
< Python code >
<<<<
3. DATA &
METHODOLOGY
Floor Area Ratio (FAR)
‣ Address
‣ Parcel Size (m2)
‣ Footprint (m2)
‣ Building Type
‣ no. of Floors
‣ Historical Buildings
‣ Boundaries: Borough 3,
White City Zone
114. gis data
< Python code >
Plan instructions
<<
<<
4.2 Predictive Analysis:
FUTURE built environment
Methodology
Writing a python code that reads real
gis data of every building and parcel
in the borough, calculates it according
to the rezoning’s instructions to
assess the allowed floor area to be
built. This to determine whether the
incentive per building is big enough to
assume it will be utilized
115. gis data
< Python code ><<
<<
4.2 Predictive Analysis:
FUTURE built environment
Plan instructions
Methodology
Writing a python code that reads real
gis data of every building and parcel
in the borough, calculates it according
to the rezoning’s instructions to
assess the allowed floor area to be
built. This to determine whether the
incentive per building is big enough to
assume it will be utilized
116. gis data
< Python code >
Anticipated floor area
<<
<<
<<
m2
** According to plan principles
4.2 Predictive Analysis:
FUTURE built environment
Plan instructions
Methodology
Writing a python code that reads real
gis data of every building and parcel
in the borough, calculates it according
to the rezoning’s instructions to
assess the allowed floor area to be
built. This to determine whether the
incentive per building is big enough to
assume it will be utilized
119. gis data
< Python code >
Anticipated floor area
<<
<<
<<
m2
** footprint (44%-56%) X
allowed # of floors
M2
2
4.2 Predictive Analysis:
FUTURE built environment
Plan instructions
Methodology
Writing a python code that reads real
gis data of every building and parcel
in the borough, calculates it according
to the rezoning’s instructions to
assess the allowed floor area to be
built. This to determine whether the
incentive per building is big enough to
assume it will be utilized
121. 4.2 Predictive Analysis:
FUTURE built environment
The far values within the white city zone are lower
than outside of it, though the histograms are
similar, meaning, the built environment will be
higher outside of the white city but the overall
coherence of the urban scape will be preserved.
123. 1’ג
38 תמ”א
S?$?
gis data
< Python code >
Anticipated floor area
feasible?
<<
<<
<<Existing floor area<<
+++
4.3 Predictive Analysis:
ECONOMIC FEASIBILITY
Considering the give backs to the dwellers of
their existing floor area (+more), the allowed
FA needs to be a large enough incentive in order
to assume the deal is economically feasible
Methodology
Writing a python code that reads real
gis data of every building and parcel
in the borough, calculates it according
to the rezoning’s instructions to
assess the allowed floor area to be
built. This to determine whether the
incentive per building is big enough to
assume it will be utilized
Plan instructions
124. 1’ג
38 תמ”א
S?$?
gis data
< Python code >
feasible?
<<
<<
<<<<
+++
4.3 Predictive Analysis:
ECONOMIC FEASIBILITY
<= 0.5Anticipated floor area
Existing floor area <500m2 : 0.45
>750m2 : 0.55
Economic feasibility threshold:
Methodology
Writing a python code that reads real
gis data of every building and parcel
in the borough, calculates it according
to the rezoning’s instructions to
assess the allowed floor area to be
built. This to determine whether the
incentive per building is big enough to
assume it will be utilized
Plan instructions
127. Will Tel-Aviv
be renewed?
4.3 Predictive Analysis:
ECONOMIC FEASIBILITY
Only 521 buildings (24% of all
the buildings that are eligible
for the rezoning) were found
profitable enough to assume
their renewal.
129. Will Tel-Aviv
be renewed?
4.3 Predictive Analysis:
ECONOMIC FEASIBILITY
3,390 housing units == 60% less than the city’s
formal prediction of 8,000 units
?Housing Units
131. Plan’s Objectives:
5 implications
?
‣ Act as a lever for urban renewal, by
upgrading non-historical buildings and
increasing the housing stock of the city
‣ Make buildings more resilient to
earthquakes according to national
masterplan no.38
‣ Preserving the spatial qualities of the
unique city center’s urban scape,
including following UNESCO’s
requirements
134. 6 future research
‣ Boroughs 4,5,6 > How will the future TLV city center’s urban scape look and function?
‣ Economic, Social, Spatial consequences
‣ Random forest, ML techniques > Which are the factors that affect the feasibility of
an incentive-based upzoning and how?
ויינברג גבריאל :צילום