| # | Title | Journal | Year | Citations |
|---|
| 1 | Digital twins | Environment and Planning B: Urban Analytics and City Science | 2018 | 205 |
| 2 | Urban planning, public participation and digital technology: App development as a method of generating citizen involvement in local planning processes | Environment and Planning B: Urban Analytics and City Science | 2019 | 107 |
| 3 | Does block size matter? The impact of urban design on economic vitality for Chinese cities | Environment and Planning B: Urban Analytics and City Science | 2019 | 96 |
| 4 | The Coronavirus crisis: What will the post-pandemic city look like? | Environment and Planning B: Urban Analytics and City Science | 2020 | 87 |
| 5 | The visual quality of streets: A human-centred continuous measurement based on machine learning algorithms and street view images | Environment and Planning B: Urban Analytics and City Science | 2019 | 75 |
| 6 | A multi-scale analysis of 27,000 urban street networks: Every US city, town, urbanized area, and Zillow neighborhood | Environment and Planning B: Urban Analytics and City Science | 2020 | 75 |
| 7 | Polycentric urban development in China: A multi-scale analysis | Environment and Planning B: Urban Analytics and City Science | 2018 | 71 |
| 8 | Household accessibility to heat refuges: Residential air conditioning, public cooled space, and walkability | Environment and Planning B: Urban Analytics and City Science | 2017 | 69 |
| 9 | Artificial intelligence and smart cities | Environment and Planning B: Urban Analytics and City Science | 2018 | 64 |
| 10 | Evaluating and characterizing urban vibrancy using spatial big data: Shanghai as a case study | Environment and Planning B: Urban Analytics and City Science | 2020 | 60 |
| 11 | How can the urban landscape affect urban vitality at the street block level? A case study of 15 metropolises in China | Environment and Planning B: Urban Analytics and City Science | 2021 | 54 |
| 12 | A catchment scale Integrated Flood Resilience Index to support decision making in urban flood control design | Environment and Planning B: Urban Analytics and City Science | 2017 | 52 |
| 13 | Urban analytics defined | Environment and Planning B: Urban Analytics and City Science | 2019 | 52 |
| 14 | Neighborhood sustainability in urban renewal: An assessment framework | Environment and Planning B: Urban Analytics and City Science | 2017 | 50 |
| 15 | Modelling the spatial accessibility of the elderly to healthcare services in Beijing, China | Environment and Planning B: Urban Analytics and City Science | 2019 | 48 |
| 16 | In search of visualization challenges: The development and implementation of visualization tools for supporting dialogue in urban planning processes | Environment and Planning B: Urban Analytics and City Science | 2017 | 45 |
| 17 | From the street to the metropolitan region: Pedestrian perspective in urban fabric analysis | Environment and Planning B: Urban Analytics and City Science | 2019 | 44 |
| 18 | Aspirations and realities of polycentric development: Insights from multi-source data into the emerging urban form of Shanghai | Environment and Planning B: Urban Analytics and City Science | 2019 | 43 |
| 19 | The role of urban form in sustainability of community: The case of Amsterdam | Environment and Planning B: Urban Analytics and City Science | 2017 | 42 |
| 20 | Street network analysis “edge effects”: Examining the sensitivity of centrality measures to boundary conditions | Environment and Planning B: Urban Analytics and City Science | 2017 | 41 |
| 21 | The platform and the bricoleur—Improvisation and smart city initiatives in Indonesia | Environment and Planning B: Urban Analytics and City Science | 2019 | 41 |
| 22 | Will coronavirus cause a big city exodus? | Environment and Planning B: Urban Analytics and City Science | 2020 | 39 |
| 23 | Dismantling the fence for social justice? Evidence based on the inequity of urban green space accessibility in the central urban area of Beijing | Environment and Planning B: Urban Analytics and City Science | 2020 | 38 |
| 24 | Beyond digital twins – A commentary | Environment and Planning B: Urban Analytics and City Science | 2019 | 37 |
| 25 | Limits of space syntax for urban design: Axiality, scale and sinuosity | Environment and Planning B: Urban Analytics and City Science | 2020 | 37 |
| 26 | A mixed methods approach for the integration of urban design and economic evaluation: Industrial heritage and urban regeneration in China | Environment and Planning B: Urban Analytics and City Science | 2018 | 36 |
| 27 | What makes a landscape contemplative? | Environment and Planning B: Urban Analytics and City Science | 2018 | 35 |
| 28 | The built environment, spatial scale, and social networks: Do land uses matter for personal network structure? | Environment and Planning B: Urban Analytics and City Science | 2018 | 35 |
| 29 | Urban infrastructure is not a tree: Integrating and decentralizing urban infrastructure systems | Environment and Planning B: Urban Analytics and City Science | 2017 | 34 |
| 30 | Form and urban change – An urban morphometric study of five gentrified neighbourhoods in London | Environment and Planning B: Urban Analytics and City Science | 2017 | 34 |
| 31 | Measuring urban form: Overcoming terminological inconsistencies for a quantitative and comprehensive morphologic analysis of cities | Environment and Planning B: Urban Analytics and City Science | 2021 | 34 |
| 32 | How walkable is Walker’s paradise? | Environment and Planning B: Urban Analytics and City Science | 2017 | 33 |
| 33 | Evaluating the scalability of public participation in urban land use planning: A comparison of Geoweb methods with face-to-face meetings | Environment and Planning B: Urban Analytics and City Science | 2019 | 33 |
| 34 | Impact-based planning evaluation: Advancing normative criteria for policy analysis | Environment and Planning B: Urban Analytics and City Science | 2019 | 33 |
| 35 | Defining urban clusters to detect agglomeration economies | Environment and Planning B: Urban Analytics and City Science | 2019 | 33 |
| 36 | Urban function recognition by integrating social media and street-level imagery | Environment and Planning B: Urban Analytics and City Science | 2021 | 33 |
| 37 | New insights on relationships between street crimes and ambient population: Use of hourly population data estimated from mobile phone users’ locations | Environment and Planning B: Urban Analytics and City Science | 2018 | 32 |
| 38 | A tool to predict perceived urban stress in open public spaces | Environment and Planning B: Urban Analytics and City Science | 2018 | 32 |
| 39 | On the origin of spaces: Morphometric foundations of urban form evolution | Environment and Planning B: Urban Analytics and City Science | 2019 | 32 |
| 40 | Validating activity, time, and space diversity as essential components of urban vitality | Environment and Planning B: Urban Analytics and City Science | 2021 | 32 |
| 41 | The scaling of income distribution in Australia: Possible relationships between urban allometry, city size, and economic inequality | Environment and Planning B: Urban Analytics and City Science | 2018 | 31 |
| 42 | Evaluating urban accessibility: leveraging open-source data and analytics to overcome existing limitations | Environment and Planning B: Urban Analytics and City Science | 2019 | 31 |
| 43 | Connecting the city: A three-dimensional pedestrian network of Hong Kong | Environment and Planning B: Urban Analytics and City Science | 2021 | 31 |
| 44 | Modeling the relationships between historical redlining, urban heat, and heat-related emergency department visits: An examination of 11 Texas cities | Environment and Planning B: Urban Analytics and City Science | 2022 | 31 |
| 45 | From paths to blocks: New measures for street patterns | Environment and Planning B: Urban Analytics and City Science | 2017 | 29 |
| 46 | A commuting spectrum analysis of the jobs–housing balance and self-containment of employment with mobile phone location big data | Environment and Planning B: Urban Analytics and City Science | 2018 | 29 |
| 47 | Measuring urban social sustainability: Scale development and validation | Environment and Planning B: Urban Analytics and City Science | 2021 | 29 |
| 48 | Streetscape skeleton measurement and classification | Environment and Planning B: Urban Analytics and City Science | 2017 | 28 |
| 49 | The influence of urban environments on our subjective momentary experiences | Environment and Planning B: Urban Analytics and City Science | 2018 | 28 |
| 50 | Patronage of urban commercial clusters: A network-based extension of the Huff model for balancing location and size | Environment and Planning B: Urban Analytics and City Science | 2018 | 28 |
