How Margit Liander brings people-first approach to urban design at Sasaki

It's not you, it's me: our new paper in Nature Cities presents the latest project from our team -- SPECS, led by Matias Quintana. We have recruited 1000 people from five continents and collected detailed data on their demographics (e.g. income, age, gender) and personality (e.g. extraversion, openness) to understand the extent these shape visual perception of streets (mostly based on our Global Streetscapes dataset containing labelled street view images from around the world). Across all cities chosen, and for a large part, the analysis revealed that differences in global urban visual perception are explained by demographics and personalities. The work introduces also some novelties for this research line, such as proposing new indicators to characterise visual perception of streetscapes. Our dataset is released openly. Thanks to Matias and the team at the NUS Urban Analytics Lab (Youlong Gu, Xiucheng Liang, Yujun Hou, Koichi Ito, Yihan ZHU, Mahmoud M. Abdelrahman) for the excellent work. Paper: https://lnkd.in/gtEb8mqS Repo: https://lnkd.in/g8X7EfTb (Street Perception Evaluation Considering Socioeconomics (SPECS))

Nature Cities published a paper by Matias Quintana, Youlong Gu, Xiucheng Liang, Yujun Hou, Koichi Ito, Yihan ZHU, Mahmoud M. Abdelrahman and Filip Biljecki on understanding how demographics and personality drive the visual perception of streetscapes. The study included surveying 1000 people from around the world. The paper asserts the importance of multi-city and multi-population analysis and it is packed with findings, e.g. that safety perceptions, in specific locations, are explained by gender. The global and, participants-wise, demographically balanced urban visual perception dataset (Street Perception Evaluation Considering Socioeconomics (SPECS)) is released openly (https://lnkd.in/gV6wdCGf). Read more: https://lnkd.in/ggZakHaV

Our cities and their streets shape human perception and behavior. Urban visual perception — that is, how people interpret and respond to visual information in city environments —has become central to understanding their urban experiences and preferences. Yet current modeling approaches often combine responses from multi-cultural populations, obscuring demographic differences and risking amplifying biases. Our recent #NatureCities paper from the Future Cities Laboratory Global at the Singapore-ETH Centre and the NUS Urban Analytics Lab at the NUS College of Design & Engineering (DOI: https://lnkd.in/gnSV8naz), co-authored with Youlong Gu, Xiucheng Liang, Yujun Hou, Koichi Ito, Yihan ZHU, Mahmoud M. Abdelrahman, and Filip Biljecki, introduces a large-scale urban visual perception survey of streetscapes worldwide using street view imagery. We examined how demographics (gender, age, income, education, race and ethnicity, and, for the first time, personality traits) shape perceptions among 1,000 participants with balanced demographics from five countries (Chile, the Netherlands, Nigeria, Singapore, and the USA). We also showed the need for more tuned models rather than one-size-fits-all models by comparing state-of-the-art perception predictions against our dataset, called Street Perception Considering Socioeconomics (SPECS) A big thank you to all my co-authors and my mentor, Filip Biljecki, for their patience throughout these (couple of) years of work! And my heartfelt gratitude to the editors and reviewers at Nature Cities, whose comments significantly helped to improve our work. 📚 Readable link: https://rdcu.be/eL8pU 💻 SPECS Dataset: https://lnkd.in/gWh_2DYv #urbaninformatics #streetviewimagery #computervision #GeoAI

🌏 👀 How do demographic and personality factors affect the way people around the world see their cities? 🕶 A new study from the Future Cities Laboratory Global [SUE] Semantic Urban Elements module was published on Nature Portfolio #NatureCities, co-authored by module coordinator Matias Quintana, intern Youlong Gu, and Principal Investigator Filip Biljecki, in collaboration with Prof. Biljecki's group at the NUS Urban Analytics Lab. The team conducted a large-scale survey of streetscapes using street view imagery, covering 1,000 participants from five countries and 45 nationalities. The resulting dataset, Street Perception Evaluation Considering Socioeconomics (SPECS), shows how demographic factors and personality traits created differences in preferences across six traditional indicators (‘safe,’ ‘lively,’ ‘wealthy,’ ‘beautiful,’ ‘boring,’ ‘depressing’) and four new ones (‘live nearby,’ ‘walk,’ ‘cycle,’ ‘green’). 🤖 The team found that ‘one-size-fits-all’ models for predicting human preferences tend to overestimate positive indicators and underestimate negative ones compared to human responses. This demonstrates the importance of accounting for people's varying preferences in the study of urban visual perception. The study shows the crucial contribution of global research to urban science, underscoring the need for cross-disciplinary and cross-context approaches to building a science of cities. Learn more from this post by Matias below. Singapore-ETH Centre National University of Singapore NUS College of Design & Engineering #streetviewimagery #urbanvisualperception #urbaninformatics #computervision #machinelearning #GeoAI

******************************The Heart of Demography****************************** The Secret Engine of Nations: It's Not Just Economics—It's Demography's Big Three. When people think of Population Studies, they often think of total numbers. But the true power of this field lies in understanding the three components of change that act as the hidden engine driving global economies, politics, and social structures. The Equation That Governs Society: The entire field boils down to one fundamental equation: P_{t+1} = P_t + B - D + I - E (Where B are births, D are deaths, I is immigration, and E is emigration.) But the real insight isn't the formula; it's the variables: >Fertility (B): This component shapes the future. Lower birth rates today mean fewer workers and more dependents 30 years from now. It defines the trajectory of the Demographic Dividend—the window of opportunity for economic growth. >Mortality (D): This component reflects past success. Falling death rates are a triumph of public health and medicine. But they create the challenge of the Aging Society, testing our healthcare and pension systems. >Migration (I/E): This component is the great equalizer and disruptor. It provides labor where it's needed but also presents challenges of integration, resource strain, and cultural shifts. It is the fastest way to change a population's composition. *The Core Components are Universal: Every aspect of population change in the world is analyzed using the "Big Three" variables: Fertility (Births), Mortality (Deaths), and Migration. This is the undisputed core of the discipline. *The Demographic Equation is Standard: The mathematical formula mentioned in the post, often called the Balancing Equation, is the standard model for calculating population change over time: Population at end = Population at start + Births - Deaths + Immigration -Emigration ------------------------------------------------------------------------------------- As I prepare for the UGC NET, my focus is on moving beyond simple definitions to modeling the relationship between these three forces and their policy consequences. Understanding how B, D, and M interact allows us to project the future and design sustainable policy. Population Studies isn't about counting heads; it's about predicting the future demands on a society. What do you believe is the single most critical demographic challenge facing our nation today—Aging, Low Fertility, or Internal Migration? Share your thoughts! 👇 #Demography #StatisticalModeling #Statistics #AppliedStatistics #FutureofWork #PopulationStudies #DataScience #PolicyMaking #UGC_NET

#30DayMapChallenge – Day 16: Cell Map. This map, titled “Nigerian Estimated Population Based on the 2006 Census,” uses a cell-based approach to visualise how people are distributed across the country. Each cell represents an estimated number of households (Each dot represents 50,000 people), revealing clear patterns of population concentration in major urban centres and densely settled regions, while also highlighting sparsely populated areas. Cell maps are useful for simplifying complex demographic data and offering an even-handed view of spatial distribution without being tied to administrative boundaries. #GIS #30DayMapChallenge #DataVisualization #PopulationMapping #Nigeria #Esri

Updates: In support of the 2027 Digital Population Census in Mozambique, the Brazilian Institute of Geography and Statistics (IBGE) and the Brazilian Cooperation Agency (ABC) are sponsoring a mission as part of the "Reference Centers for Censuses with Electronic Data Collection in Africa - Phase II" program. From October 20 to 30, a training mission will be in Mozambique as part of this project. The mission will consist of technicians from IBGE, ANSD, INECV, INE, UNFPA Brazil, and UNFPA Mozambique. United Nations Population Fund (UNFPA) IBGE-Instituto Brasileiro de Geografia e Estatística Brazilian Cooperation Agency (ABC)

This post helps students and professionals analyze population pyramids effectively. Understand visual cues to determine population trends, growth rates, and demographic stability — essential for geography, demography, and social science exams.

This post helps students and professionals analyze population pyramids effectively. Understand visual cues to determine population trends, growth rates, and demographic stability — essential for geography, demography, and social science exams.

Population Density Map of Bangladesh Bangladesh is one of the most densely populated countries in the world, and its population density map tells a powerful story. The highest concentrations of people are found around Dhaka, Narayanganj, and Chittagong, while the Sundarbans and hill tracts in the southeast remain sparsely populated. With over 170 million people living in a relatively small land area, Bangladesh’s map highlights both its vibrant human energy and the challenges of urbanization, resource management, and climate resilience. #Bangladesh #PopulationDensity #Geography #Demographics #Dhaka #Chittagong #Urbanization #SouthAsia #Mapping #Sustainability