The theory: Jane Jacobs One of the most influential books in city planning • planning models that dominated mid-century planning • American housing policy (HOPE VI) • Melbourne, Toronto etc. 79 1 2 3 4 THEORY TEST Klemek, C. (2011) ‘Dead or Alive at Fifty? Reading Jane Jacobs on her Golden Anniversary’ Dissent, Vol. 58, No. 2, 75–79.
The theory: not tested! • Not empirically tested until 2015 • Tested in Seoul, from costly surveys collected in years • Theory from 1961! 80 1 2 3 4 THEORY TEST Sung, Hyungun, Sugie Lee, and SangHyun Cheon. "Operationalizing Jane Jacobs’s Urban Design Theory Empirical Verification from the Great City of Seoul, Korea." Journal of Planning Education and Research (2015.
The theory: Jane Jacobs One of the most influential books in city planning • Death: caused by the elimination of pedestrian activity • Life: created by a vital urban fabric at all times of the day 81 1 2 3 4 THEORY TEST
Jacobs’ diversity conditions Diversity => Urban vitality There are 4 diversity conditions To be ensured in each city’s district (10,000+ inhabitants) 82 1 2 3 4 THEORY TEST SMALL BLOCKS LAND USE AGED BUILDINGS DENSITY
Land Use Mix 2+ primary uses (contemporarily) JACOBS’ VIEW: People come for different purposes, continuously EFFECT: “sidewalk ballet” and “eyes on the street” 83 LAND USE SMALL BLOCKS AGED BUILDINGS DENSITY 1 2 3 4 THEORY TEST
Small blocks City blocks should be small/short LAND USE SMALL BLOCKS 84 1 2 3 4 THEORY TEST AGED BUILDINGS DENSITY JACOBS’ VIEW: improves walkability EFFECT: Increase face-to-face interactions
Aged buildings Buildings mixed (age and types) 85 AGED BUILDINGS 1 2 3 4 THEORY TEST LAND USE DENSITY SMALL BLOCKS JACOBS’ VIEW: To ensure economic diversity EFFECT: high-/low-income residents new/small enterprises
Density Concentration of people and enterprises JACOBS’ VIEW: People have a reason to live in a district EFFECT: Attract people 86 SMALL BLOCKS DENSITY 1 2 3 4 THEORY TEST LAND USE AGED BUILDINGS
Border Vacuums • Patches of land dedicated to one single use • They could be either bad and good: • Parks are good for pedestrian activity • But they are exposed to criminality and deprivation if not well managed (e.g. night) 88 LAND USE SMALL BLOCKS AGED BUILDINGS DENSITY VACUUMS 1 2 3 4 THEORY TEST
“Operationalize” Land Use Mix For district : % = − ( )∈+ %,) log(%,) ) log || %,): % square footage of land use : {residential, commercial, recreation} 99 LAND USE SMALL BLOCKS AGED BUILDINGS DENSITY VACUUMS 1 3 4 THEORY TEST 2 Ref: R. Cervero. Land-use mixing and suburban mobility. University of California Transportation Center, 1989 EFFECT: The higher, the better. 1 0
“Operationalize” Small blocks Block size is a proxy for an high number of peoples’ interactions For district : 1 | | ( =∈=>?@AB(%) () EFFECT: The lower, the better 103 LAND USE SMALL BLOCKS AGED BUILDINGS DENSITY VACUUMS 1 3 4 THEORY TEST 2
Aged buildings For district the weighted standard deviation of buildings age. EFFECT: The higher, the better 107 LAND USE SMALL BLOCKS AGED BUILDINGS DENSITY VACUUMS 1 3 4 THEORY TEST 2 MNO = ∑HQR S TH(UHV ̅ U)X Y (Z[R) Z ∑HQR S TH
“Operationalize” Density For district : Employment density: |\]^>?_O` ^O?^>OH| MaOMH Population density: |b?^c>Md%?eH| MaOMH EFFECT: The higher, the better 110 LAND USE SMALL BLOCKS AGED BUILDINGS DENSITY VACUUMS 1 3 4 THEORY TEST 2
“Operationalize” Vacuums Distance to huge parks for district : 1 % ( )∈gH ( , , ) % : the set of the blocks : the set of parks EFFECT: The higher, the better 112 LAND USE SMALL BLOCKS AGED BUILDINGS DENSITY VACUUMS 1 3 4 THEORY TEST 2
Call Detail Records Data collected by mobile operators for billing reasons • Unique userID • Gender and age • Geographical location (Antenna) • Datetime 115 1 2 3 4 THEORY TEST
“Operationalize” Vitality • Mobile phone Internet activity as a proxy for urban vitality • We calculate the activity density in each district 1 || ( l∈m | % | : set of hours (180 days x 24h) • Six Italian cities with 100,000+ inhabitants (e.g. Rome, Milan…) • 6 months time span (in 2014) 117 0.2 0.0 0.2 0.4 0.6 0.8 1.0 Land Use Mix 0.8 1.2 1.8 2.7 4.1 6.1 9.3 14.0 21.1 31.9 Activity density ⇥ 103 ROME MILAN 0.8 1.2 1.8 2.7 4.1 6.1 9.3 14.0 21.1 31.9 Activity density 103 1 2 3 4 THEORY TEST MILAN
−3 −2 −1 0 1 2 3 −9 −8 −7 −6 −5 −4 −3 −2 . Is the theory still valid? 121 Intersections density (log + Z-score) Activity density (log) 1 2 3 4 THEORY TEST
−3 −2 −1 0 1 2 3 −9 −8 −7 −6 −5 −4 −3 −2 Is the theory still valid? 122 Intersections density (log + Z-score) Activity density (log) 1 2 3 4 THEORY TEST
−3 −2 −1 0 1 2 3 −9 −8 −7 −6 −5 −4 −3 −2 R2 : 0.63 Is the theory still valid? 123 Intersections density (log + Z-score) Activity density (log) 1 2 3 4 THEORY TEST
Why does it matter? • Evaluate the districts vitality • Know in advance the best places for retails • Quantifying regulatory interventions • We created the recipe for city that works 134 1 2 3 4 THEORY TEST
Broken windows theory • City mismanagement • Dirty places • Poor infrastructure Lead to misbehavior => Crime Q: Are people avoiding places where they feel unsafe? 137 Wilson, James Q., and George L. Kelling. "Broken windows." Critical issues in policing: Contemporary readings (1982): 395- 407.
138 Urban perception from Place Pulse Salesses, P., Schechtner, K., & Hidalgo, C. A. (2013). The collaborative image of the city: mapping the inequality of urban perception. PloS one
140 Security perception prediction * B. Zhou, A. Lapedriza, J. Xiao, A. Torralba, and A. Oliva. “Learning Deep Features for Scene Recognition using Places Database.” NIPS, 2014. • Learning human security perception PERCEPTION SCORE [0-10]
142 Urban metric Standardized Beta coefficient Population density 0.155** Employees density 0.328** Deprivation -0.022 Distance from the center -0.257** Security perception 0.105** adj − Rs 0.91 ** p-value < 0.001; * p-value < 0.01; Security perception -> presence of people
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