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Published onMar 29, 2019

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311 apps, 40–41, 42, 43, 45, 47–49, 51, 60, 73, 160

1984 (Orwell), 96

Addis Ababa (Ethiopia), 157

Aerial perspective, 147, 148, 149

Aircraft (Le Corbusier), 148

Algorithms, 1–2, 3, 49–50, 87–88, 95, 97, 103–107, 123, 161. See also Machine learning

Alphabet (Google’s parent company). See Sidewalk Labs

Amazon, 69, 99

Anderson, Chris, 67. See also Wired

AppCivist, 59

Apple, 41

Apps, civic, 3, 40–43

311 apps, 40–41, 42, 43, 45, 47–49, 51, 60, 159–160

AppCivist, 59

Brigade (Parker), 40, 42

parking, 113 (Daschle), 40, 42

Textizen, 40, 43

Argonne National Laboratory, 107

Arnold Foundation. See Laura and John Arnold Foundation

Array of Things (AoT), 107–109. See also Internet of Things

Artificial intelligence, 3, 95

Automatic license plate readers (ALPRs), 98, 100

Automobiles, 16, 18–21, 24, 26–27, 143–144. See also Autonomous vehicles; Traffic

Autonomous vehicles (AVs), 8, 15–18, 24–30, 35–37, 68, 157. See also Massachusetts Institute of Technology: Senseable City Lab; Traffic

Bailo, Carla, 32, 33, 34, 159. See also Columbus

Baldwin, James, 151

Baltimore (MD), 41

Barcelona (Spain), 115, 158

Baumgardner, Kinder, 16, 17

Bayley, David, 77

Bell, Monica, 46

Berk, Richard, 83

Berman, Marshall, 8, 52

Beware (Intrado), 104

Big Brother, 96. See also Surveillance

Big Data, 49, 67, 70, 153

Bike share data, 95, 96

Black Lives Matter, 96, 97

Body cameras, 98–99. See also Policing

Boldin, Shakira, 131, 135, 137

Bonaguro, Joy, 126–130, 138, 162. See also San Francisco

Boston (MA), 1, 24, 48, 53, 54, 156–157, 159

311 app, 51

Boston Public Schools, 54

“Boston Smart City Playbook” (2016), 156

Department of Innovation and Technology, 134

Mayor’s Office of New Urban Mechanics (MONUM), 113, 140–141

Brady, Henry, 45

Brasília (Brazil), 149–150, 151

South Wing, 149

Brasilite, 150

Brazil, 58. See also Brasília; Rio de Janeiro

participatory budgeting (PB), 58, 157–158

Brigade (Parker), 40, 42

Bronx, 118, 121–124. See also New York City

Buckley, Steve, 15–16, 18, 28–29, 30. See also Toronto

Burns, Nancy, 45

Cambridge (MA), 63, 65, 67

Police Department (CPD), 63–66, 70, 85

Caro, Robert, 21–22, 150. See also Moses, Robert

Carr, Kerstin, 31, 32. See also Columbus

Cars. See Automobiles

Chasnoff, John, 78

Chicago (IL), 11, 72, 103, 107–109, 112, 161

311 app, 41

Department of Public Health (CDPH), 139–140

Police Department, 72, 86–87, 103. See also Strategic Subjects List

Cisco, 3, 6, 8

Columbus (OH), 30–35, 37, 159. See also Smart City Challenge; Smart Columbus

Easton, 32

Linden, 32–34

Columbus Buggy Company (CBC), 35

Community PlanIt (CPI), 53–55, 58–59, 160. See also Gordon, Eric; Walter, Steve

COMPAS (Northpointe), 104–105

Contracts. See Government contracts

Costa, Lúcio, 150. See also Brasília

Crime. See Policing

Cyberattacks, 102. See also Data

Daschle, Nathan, 40, 41–42

Data, 49–50, 97

anonymous, 93–95, 101–102

Array of Things (AoT), 107–109

behavioral, 94

bike share programs, 95, 96

brokers, 98, 99, 100

collection, 2, 9, 80, 93–103, 106–114, 138, 155, 158, 161 (see also LinkNYC)

cyberattacks, 102

drills, 124–125

hacks and security breaches, 102

integration, 80, 121–125, 129, 132, 162

judicial sentencing, influence on, 106

minimization, 108–109, 111

monetization, 9, 93, 95, 99

quality, 127–128, 162

racial bias, 2, 68–69, 72–73, 87, 103

release of, 94–95

training data, 65, 66–69, 70, 72–73, 81, 87, 103, 139–140

Data-Driven Justice Initiative, 78, 80, 81

Datasets, 80, 82, 93, 94–95, 101, 125, 126

DataSF, 127–128

Legionnaires’ disease outbreak, 119, 121–122, 124,

DataSF, 127–131, 137. See also San Francisco

Data Academy, 128, 129

Davis, Jordan, 30, 31, 32, 35, 37. See also Columbus

Death and Life of Great American Cities (Jacobs), 151

De Blasio, Bill, 92, 106. See also New York City

De Montjoye, Yves-Alexandre, 93–94

Department of Homeland Security. See U.S. Department of Homeland Security

Department of Justice. See U.S. Department of Justice

Department of Transportation (DOT). See U.S. Department of Transportation

Determinism. See Technological determinism

Detroit (MI), 51, 54, 92

Dewey, John, 4

Direct democracy, 39, 40, 43

Doctoroff, Dan, 93, 143–144, 154. See also Sidewalk Labs

Domain Awareness System (IBM), 84

Downs, Anthony, 22

Driverless cars. See Autonomous vehicles

DriveWAVE, 25. See also Massachusetts Institute of Technology: Senseable City Lab

Duranton, Gilles, 22–23

Electronic benefits transfer (EBT) cards, 97. See also Welfare; WIC (Women, Infants, and Children) program

Email. See Gmail

Emerson College, 53

Engagement Lab, 53

Eno Center for Transportation, 16

Equivant. See Northpointe

Eubanks, Virginia, 97, 98

Facebook, 40, 95, 96, 97. See also Social media

Facial recognition software, 99

Federal Bureau of Investigation (FBI), 96

Federal Communications Commission, 114

Ferguson, Andrew, 70, 88

Ford, Henry, 35

Ford Motor Company, 26, 35

Fort Greene Houses, 151. See also New York City

Fountain, Jane, 47

Franklin-Hodge, Jascha, ix–xi, 156–157, 159. See also Boston

Fry, Christopher, 42

Fung, Archon, 43

Futurama, 17, 20

Games and playful learning, 52, 54, 55. See also Community PlanIt; Walter, Steve

Garden Cities of To-Morrow (Howard), 147

Garden City, 147–148

Garner, Eric, 48. See also Policing; Racial bias

General Motors Corporation, 17

Germany, 144–146

Gilliom, John, 97

Gilman, Hollie Russon, 43, 56–58, 59, 61. See also Participatory budgeting

Gmail, 66–68

Goldstein, Brett, 72

Google, 15. See also Gmail; Sidewalk Labs

Gordon, Eric, 53, 55, 56, 58

Government contracts, 133–136

Government Performance Lab, 132. See also Grover-Roybal, Christina

Governments, and lack of transparency, 103–104, 106

Greenfield, Adam, 6

Groman, Marc, 113

Grover-Roybal, Christina, 132–133, 134–135, 136–137. See also Seattle

Gun violence, 86, 87. See also Strategic Subjects List

Hacks and security breaches, 102. See also Data

Han, Hahrie, 44, 58

Hattiesburg (MS), 112

Heffner, Jeremy, 73–76. See also HunchLab

Historical data. See Training data

Hitachi, 72, 153

Hollywood, John, 71

Holston, James, 150. See also Brasília

Homelessness, 11, 77, 79, 131–138

Howard, Ebenezer, 147–148, 150, 151

Human Rights Data Analysis Group, 73

HunchLab, 73–76

IBM, 6, 41, 84

Immigration and Customs Enforcement (ICE). See U.S. Immigration and Customs Enforcement

Induced demand, 22–23. See also Traffic

InLinkUK, 158. See also LinkNYC

Insight2050 (Mid-Ohio Regional Planning Commission), 31, 32. See also Columbus

Instagram, 95. See also Social media

Internet access, 33, 45, 92, 100. See also LinkNYC; Wi-Fi

Internet of Things (IoT), 98, 102, 153, 156, 158. See also Array of Things

Interstate Highway System, 21

Intrado, 103

Jacob, Nigel, 113, 141, 156. See also Boston: Mayor’s Office of New Urban Mechanics

Jacobs, Jane, 151–152, 153, 157

Jeanneret, Charles-Édouard. See Le Corbusier

Johnson, Jason, 131–132, 133, 135, 136, 138. See also Seattle

Johnson County (KS), 11, 78, 80–82, 88, 161

Judicial sentencing, 9, 104–106

Kafka, Franz, 96, 104

King, Martin Luther, Jr., 96

Klockars, Carl, 72

La Crosse (WI), 104

Lane, Alyssa, 59, 60–61. See also Vallejo (CA)

Lanyon, Ryan, 16, 18, 28, 29. See also Toronto

Latour, Bruno, 4, 42

Laura and John Arnold Foundation, 81–82. See also Data-Driven Justice Initiative

Law enforcement. See Policing

Le Corbusier, 148–149, 150, 151, 152, 155

Legionnaires’ disease, 118–119, 121, 123–124, 130–131. See also New York City

Lieberman, Henry, 42

Lincoln (NE), 41

LinkNYC, 2, 9, 92–93, 95, 99, 100, 102–103, 107–108, 115, 155, 158. See also Array of Things; InLinkUK; Sidewalk Labs

Living PlanIt, 153

London (United Kingdom), 95, 158

Loomis, Eric, 104. See also COMPAS

Los Angeles (CA), 41, 98. See also U.S. Immigration and Customs Enforcement

Lucas, Kim, 141. See also Boston: Mayor’s Office of New Urban Mechanics

Lyft, 26–27, 33. See also Uber

MAC addresses, 93, 99–100, 111. See also Privacy

Machine learning, 66–67, 68, 70, 72, 153

calibrated predictions, 105

Chicago Police Department, Strategic Subjects List, 86–87, 103

crime patterns, 1–2, 63–67, 71

fairness, 68–69, 71, 73–74, 76–77, 83, 86, 105, 107

judicial sentencing, 9, 104–106

and Legionnaires’ disease, 123–124

mental health and social services, 80–81

prediction and social media, 95

public health, 139–140

racial bias, 1–2, 8, 67, 69, 73, 84–87, 91, 103–105

St. George’s Hospital Medical School, 69, 73

surveillance and criminalization, 86, 100, 112

training data, 66–68, 140

Malinowski, Sean, 72

Masdar City (United Arab Emirates), 154

Mashariki, Amen Ra, 118–125, 130–131, 162. See also New York City

Massachusetts Institute of Technology (MIT), 42,

predictive policing algorithm, 64, 67

Senseable City Lab, 1, 16–17, 24, 25, 152

Mattmiller, Michael, 109, 110, 111. See also Seattle

McKesson, DeRay, 96

Melle, Laura, 134. See also Government contracts

Mental health, 11, 77–81, 86, 88, 161

Metcalf, Jacob, 73

Metrics, 129–130

Miami (FL), 79

Miami-Dade Police Department, 79

Mid-Ohio Regional Planning Commission (MORPC), 31. See also Insight2050

Misa, Thomas, 35

MIT. See Massachusetts Institute of Technology

Model T, 35

Modernism, high, 146–152

Morgan Stanley, 17

Morozov, Evgeny, 5. See also Solutionism

Moses, Robert, 7, 21–22, 150–151, 152

Motor Age, 17, 18, 20, 36, 143–144

NDAs. See Nondisclosure agreements

Needham, Catherine, 47

New Inquiry, 72

New Orleans (LA), Police Department, 112

Newsom, Gavin, 40

New York City (NY), 11, 106, 161

311 app, 48, 51

Bronx, 118, 121–124

Bronx-Whitestone Bridge, 22

data drills, 124–125

data release, 94–95

Department of Buildings (DOB), 121, 122

Department of Finance (DOF), 121, 122

Department of Health and Mental Hygiene (DOHMH), 118, 122, 130

Department of Housing Preservation and Development, 125

Fort Greene Houses, 151

Garner, Eric, death of, 48

Grand Central Parkway, 21, 22

Hurricane Sandy, 51

Interborough Parkway, 21

internet access (broadband), 92 (see also LinkNYC)

Laurelton Parkway, 21

Legionnaires’ disease, 118–119, 121, 123–124, 130

LinkNYC, 2, 9, 92–93, 95, 99, 100, 102–103, 107–108, 155, 158 (see also Array of Things)

Mayor’s Office of Data Analytics (MODA), 121, 122–123, 125, 126, 130–131, 137

Moses, Robert, and urban renewal efforts, 7, 150–151

New York City Council, 106

NYC Emergency Management (NYCEM), 118, 122, 124, 130

Sidewalk Labs, 2, 93, 95 (see also LinkNYC)

Triborough Bridge, 22

Wantagh State Parkway Extension, 22

Wi-Fi, public, 2 (see also LinkNYC)

New York Civil Liberties Union, 95

Niemeyer, Oscar, 150. See also Brasília

Nondisclosure agreements, 103

Northpointe, 104–105, 115

Norton, Peter, 18–19

Oakland (CA), 73, 112

Police Department, 73–75

Obama, Barack, Administration, 3, 81

Oceanside (CA), 102

Office of Personnel Management (OPM). See U.S. Office of Personnel Management

Ohio State University, 32

OkCupid, 97

Open data initiatives, 101

Orlando (FL), 99

Orwell, George, 96

Overmann, Lynn, 78, 79–80. See also Data-Driven Justice Initiative

Palmisano, Samuel, 6

Papachristos, Andrew, 86

Parker, Sean, 40, 42

Participatory budgeting (PB), 56–61, 157–158, 160

Pasquale, Frank, 49

Personally identifiable information (PII), 93, 95, 102, 110

Phoenix (AZ), 27

Play. See Games and playful learning

Policing. See also Mental health; Predictive recidivism; Racial bias

Black Lives Matter, 96, 97

body cameras, 98–99

Cambridge Police Department (CPD), 63–64, 65, 66, 69–70, 85

Chicago Police Department, 71–72, 86–87

crime databases, 63–64, 65

crime hot spots, myth of, 8, 72–75, 76, 77

crime maps, 72–75

crime-pattern algorithms, 1–2, 63–67, 71

facial recognition software, 99

Garner, Eric, death of, 48

mental health and jail populations, 78–81

Miami-Dade Police Department, 79

New Orleans Police Department, 112

Oakland Police Department, 73–75

Orlando Police Department, 99

poor and minority communities, 46, 72–75, 85–86

predictive policing, 1–2, 8, 70–78, 82, 84–88, 103–105, 112, 158, 160

racial bias, 2, 8, 46, 70–77, 87, 88, 96, 97, 160

Seattle Police Department, 109–110, 112

stop-and-frisk, 77, 85

surveillance, 72, 86–87, 99

threat scores, 103

violence against activists, 96

Political apathy, 45–46

Porter, Theodore, 7

Predatory loans, 98, 100

Predictive policing, 1–2, 8, 70–77, 82, 84–88, 103–105, 112, 158, 160. See also Policing

Predictive recidivism, 104–105

PredPol, 70, 71, 72, 73, 82, 85, 104

Privacy, 93–98, 100, 102, 107–114, 161. See also Array of Things; LinkNYC

Privacy Advisory Committee, 110–111. See also Seattle

“Privacy Impact Assessment,” 110

Procurement. See Government contracts

ProPublica, 105

Racial bias, 2, 7, 8, 46, 48, 68–69, 84

algorithms, 1–2, 8, 67, 69, 73, 84–87, 103–105

data, 2, 68–69, 72–73, 87, 103

machine learning, 69, 72–73, 87, 91

policing, 2, 8, 46, 70–77, 87, 88, 96, 97, 160

urban renewal, 7, 150–151

water rights, 7

Radiant City, 148–149, 152

RAND Corporation, 71, 76, 86

Reasonocracy, 42. See also Solutionism

Representative democracy, 39–40, 41, 53

Rio de Janeiro (Brazil), 150

Rittel, Horst, 4–5

Robin, Corey, 42

Robinson, David, 70, 71, 82 (Daschle), 40, 42

Rudin, Cynthia, 64, 65, 66

Rushkoff, Douglas, 103

Sadowski, Jathan, 49

Salisbury, Harrison, 151

San Francisco (CA), 11, 126–127

DataSF, 127–131, 137

Department of Public Health, 130

Mayor’s Office of Housing and Community Development, 130

open data portal, 127

WIC (Women, Infants, and Children) program, 130

Schenk, Tom, 138–140. See also Chicago

Schiener, Dominik, 39–40, 42, 43–44

Schlozman, Kay, 45

Schneier, Bruce, 102

Scott, James C., 145, 146, 147

Seattle (WA), 109–112, 161

Department of Transportation (SDOT), 111

government contracts, 133–136

homelessness, 11, 131–138

Human Services Department (HSD), 131–133, 134, 135–138

Police Department, 109–110, 112

Privacy Advisory Committee, 110–111

“Privacy Impact Assessment,” 110

surveillance technology ordinance, 112

United Way, 136

Young Women’s Christian Association (YWCA), 135

Self-driving cars. See Autonomous vehicles

Sevieri, Rich, 63–64, 65, 66, 67, 71. See also Cambridge (MA)

Shkabatur, Jennifer, 43

Shreveport (LA), 71, 76

Sidewalk Labs, 2, 93, 95, 99, 115, 117, 143, 154, 155. See also LinkNYC

computational vision of the city, 153

Silicon Valley, 154

Singapore, 157

Smart cities, 2–4, 6–13, 30, 39, 87, 98–100, 127, 137, 143–144, 152, 161. See also Technology goggles

general description of, 3

Greenfield on, 6

Masdar City (United Arab Emirates), 154

public-private partnerships, 100–101 (see also LinkNYC)

social control, 92, 100, 161

Songdo (South Korea), 154

surveillance, 92, 100, 161

undemocratic, 92, 103

Y Combinator, 153–154, 157

Smart City Challenge, 30, 32. See also Columbus

Smart Columbus, 30, 32, 33, 34, 159. See also Columbus

Smart Enough Cities, 10–12, 34, 37, 86, 107, 112, 114, 137. See also Chicago;

Johnson County (KS); New York City; San Francisco; Seattle

principles of, 158–162

Smart policing. See Policing

Social Apps Lab, 59

Social media, 40, 49, 55

Facebook, 40, 95, 96, 97

Instagram, 95

political engagement, 40, 41–42, 97

surveillance, 112

Solove, Daniel, 95, 96, 104

Solutionism, 4–5, 37, 46–47. See also Technological determinism; Technologists

Songdo (South Korea), 154

Spam email, 66–68

Speck, Jeff, 28

Stanford Crowdsourced Democracy Team, 59

Stanford University, 59

St. George’s Hospital Medical School, 69, 73

Stop-and-frisk, 77, 85. See also Policing

Strategic Subjects List (SSL), 86–87, 103. See also Chicago: Police Department

Streets, 18–19

Sullivan, Robert, 82, 88. See also Johnson County (KS)

Surveillance, 95, 96, 98, 112

Black Lives Matter, 96, 97

body cameras, 98–99

corporate, 2 (see also LinkNYC; Sidewalk Labs)

policing, 72, 86–87, 99

smart cities, 92, 100, 161

social media, 112

Xinjiang (China), 158

Sweeney, Latanya, 94

Synchro, 21

Tauberer, Joshua, 51

Tech goggles. See Technology goggles

Technocrats. See Technologists

Technological determinism, 7, 9. See also Solutionism; Technologists

Technologists, 39, 40, 43, 46–47, 49–51, 60, 61, 67, 113, 120, 144–149, 155. See also Solutionism; Technological determinism; Technology goggles

laws, scorn for, 149, 154

Technology goggles (tech goggles), 4–6, 7, 9, 91, 144–146, 158, 159, 161, 162

cycle, 5

democracy and politics, 39, 42, 47, 50, 51, 55

optimization and urban planning, 151, 152–154, 157

policing, 71, 82, 85, 88

privacy and innovation, 112

transportation and autonomous vehicles (AVs), 19, 27, 35–37

Tempe (AZ), 27, 68

Textizen, 40, 43. See also Apps, civic

Tocqueville, Alexis de, 41

Toronto (Canada)

Automated Vehicles (AV) Working Group, 16, 28, 29–30 (see also Buckley, Steve; Lanyon, Ryan)

autonomous vehicles (AVs), 15–16, 18, 28–30

Sidewalk Labs, 117, 154–155

Traffic. See also Automobiles; Autonomous vehicles

accidents, 16, 18

engineers, 18–19, 21, 22, 23

induced demand, 22–23

MAC addresses, 111

models, 21

optimization, 8, 19, 21, 26–28, 36 (see also Massachusetts Institute of Technology: Senseable City Lab)

parking, 17, 24, 29, 113, 157

Seattle Department of Transportation (SDOT), 111

urban patterns, 22–23

Training data, 65, 66–69, 70, 72–73, 81, 87, 103, 139–140. See also Data; Machine learning; Racial bias

Trial, The (Kafka), 96, 104

Turner, Matthew, 23

Uber, 15–16, 33, 68, 102, 115. See also Lyft

U.K. Commission for Racial Equality, 69


United Way, 136

University of California, Berkeley, 59

University of Chicago, 80, 81, 107

Data Science for Social Good program, 80 (see also Machine learning)

University of Pennsylvania, 83

Urban planning, 144, 147–148, 150, 152. See also Urban renewal

Urban renewal, 7, 150–151. See also Moses, Robert

U.S. Department of Homeland Security, 109

U.S. Department of Justice, 79

U.S. Department of Transportation (DOT), 30, 31

U.S. Immigration and Customs Enforcement (ICE), 98

U.S. Office of Personnel Management (OPM), 119, 120, 121

Vacca, James, 106–107. See also New York City

Vallejo (CA), 59, 60, 160

Vaithianathan, Rhema, 49–50

Verba, Sidney, 45

Vitale, Alex, 86

Wagner, Dan, 63, 64, 65, 66, 67, 71, 85, 86–87. See also Cambridge (MA)

Wall Street Journal, 158

Walsh, Thea, 31, 32, 34. See also Columbus

Walter, Steve, 51–53, 54–56, 58. See also Boston; Community PlanIt

Wang, Tong, 64, 65, 67

Webber, Melvin, 4–5

Weiss, Mitch, 51

Weld, William, 94

Welfare, 97–98, 100. See also Electronic benefits transfer cards; WIC (Women, Infants, and Children) program

“White Collar Crime Early Warning System,” 72

White House Office of Science and Technology, 79. See also Overmann, Lynn

WIC (Women, Infants, and Children) program, 130. See also Electronic benefits transfer cards; Welfare

Wi-Fi, 2, 9, 33, 92, 99, 100. See also Internet access; LinkNYC

Winner, Langdon, 5–6, 7, 91

Wired, 67, 138

World’s Fair (New York City, 1939), 17. See also Futurama

Xinjiang (China), 158

Y Combinator, 153–154, 157

Yoder, Steve, 81, 82. See also Johnson County (KS)

Young Women’s Christian Association (YWCA), 135. See also Homelessness; Seattle

Zimmer, John, 27

Zuckerberg, Mark, 40

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Peter Nõu:

Ben! I read and liked Greenfields book 6 years ago. Found it, however, very much ’against’. Stop! Your Thesis is more similar but still ’hold your horses’. Are you ok with that description? How do you find readers and tracition inside tech? Or inside ’Non tech sides of local gov’? As I said, I read and appreciate both your perspectives. But i have a hard time getting others on either side to even engage with this gray middle matter. Techies stay in their overly positive narrative, and non techies do. Not. Engage. With anything digital yet. They stay away from tech altogether and embrace other objectives for the city\s development, such as sustainability.

How do we engage a wider audience in the stuff that you so eloquently describe? How can I help? How can we get more purposeful development Strategies, rejecting tech goggles while not (seeming to) being reactionary