A new era is presently unfolding wherein both smart urbanism and sustainable urbanism processes and practices are becoming highly responsive to a form of data-driven urbanism under what has to be identified as data-driven smart sustainable urbanism. This flourishing field of research is profoundly interdisciplinary and transdisciplinary in nature. It operates out of the understanding that advances in knowledge necessitate pursuing multifaceted questions that can only be resolved from the vantage point of interdisciplinarity and transdisciplinarity. This implies that the research problems within the field of data-driven smart sustainable urbanism are inherently too complex and dynamic to be addressed by single disciplines. As this field is not a specific direction of research, it does not have a unitary disciplinary framework in terms of a uniform set of the academic and scientific disciplines from which the underlying theories can be drawn. These theories constitute a unified foundation for the practice of data-driven smart sustainable urbanism. Therefore, it is of significant importance to develop an interdisciplinary and transdisciplinary framework. With that in regard, this paper identifies, describes, discusses, evaluates, and thematically organizes the core academic and scientific disciplines underlying the field of data-driven smart sustainable urbanism. This work provides an important lens through which to understand the set of established and emerging disciplines that have high integration, fusion, and application potential for informing the processes and practices of data-driven smart sustainable urbanism. As such, it provides fertile insights into the core foundational principles of data-driven smart sustainable urbanism as an applied domain in terms of its scientific, technological, and computational strands. The novelty of the proposed framework lies in its original contribution to the body of foundational knowledge of an emerging field of urban planning and development.

There has been mounting interest about how the repurposing of vacant land (VL) through green infrastructure (the most common smart decline strategy) can reduce stormwater runoff and improve runoff quality, especially in legacy cities characterized by excessive industrial land uses and VL amounts. This research examines the long-term impacts of smart decline on both stormwater amounts and pollutants loads through integrating land use prediction models with green infrastructure performance models. Using the City of St. Louis, Missouri, USA as the study area, we simulate 2025 land use change using the Conversion of Land Use and its Effects (CLUE-S) and Markov Chain urban land use prediction models and assess these change’s probable impacts on urban contamination levels under different smart decline scenarios using the Long-Term Hydrologic Impact Assessment (L-THIA) performance model. The four different scenarios are: (1) a baseline scenario, (2) a 10% vacant land re-greening (VLRG) scenario, (3) a 20% VLRG scenario, and (4) a 30% VLRG scenario. The results of this study illustrate that smart decline VLRG strategies can have both direct and indirect impacts on urban stormwater runoff and their inherent contamination levels. Direct impacts on urban contamination include the reduction of stormwater runoff and non-point source (NPS) pollutants. In the 30% VLRG scenario, the annual runoff volume decreases by 11%, both physical, chemical, and bacterial pollutants are reduced by an average of 19%, compared to the baseline scenario. Indirect impacts include reduction of the possibility of illegal dumping on VL through mitigation and prevention of future vacancies.

Urban perception is a hot topic in current urban study and plays a positive role in urban planning and design. At present, there are two methods to calculate urban perception. 1) Using a model to learn image features directly automatically; 2) Coupling machine learning and feature extraction based on expert knowledge (e.g. object proportion) method. With two typical streets in Wuhan as the study area, video data were recorded and used as the model input. In this study, two representative methods are selected: 1) End to end convolution neural network (CNN-based model); 2) Based on full convolution neural network and random forest (FCN + RF-based model). By comparing the accuracy of two models, we analyze the adaptability of the model in different urban scenes. We also analyze the relationship between CNN-based model and urban function based on POI data and OSM data, and verify its interpretability. The results show that the CNN-based model is more accurate than FCN + RF-based model. Because the CNN-based model considers the topological characteristics of the ground objects, its perception results have a stronger nonlinear correlation with urban functions. In addition, we also find that the CNN-based model is more suitable for scenes with weak spatial heterogeneity (such as small and medium-sized urban environments), while the FCN + RF-based model is applicable to scenes with strong spatial heterogeneity (such as the downtown areas of China’s megacities). The results of this study can be used as a reference to provide decision support for urban perception model selection in urban planning.

Digital twins are considered to be a new starting point for today’s smart city construction. This paper defines the concepts of digital twins and digital twin cities, discusses the relationship between digital twins and smart cities, analyzes the characteristics of smart cities based on digital twins, and focuses on the five main applications of smart cities based on digital twins. Finally, we discuss the future development of smart cities based on digital twins.

In urban renewal processes, metro line systems are widely used to accommodate the massive traffic needs and stimulate the redevelopment of the local area. The route choice of pedestrians, emanating from or going to the metro stations, is influenced by the street-scale built environment. Many renewal processes involve the improvement of the street-level built environment and thus influence pedestrian flows. To assess the effects of urban design on pedestrian flows, this article presents the results of a simulation model of pedestrian route choice behavior around Yingkoudao metro station in the city center of Tianjin, China. Simulated pedestrian flows based on 4 scenarios of changes in street-scale built environment characteristics are compared. Results indicate that the main streets are disproportionally more affected than smaller streets. The promotion of an intensified land use mix does not lead to a high increase in the number of pedestrians who choose the involved route when traveling from/to the metro station, assuming fixed destination choice.

Hukou reform and relevant policies are being implemented in current China, but the response of migrants to hukou conversion in destinations falls short of expectation. It is significant to understand how contextual factors in destination cities affect migrants’ willingness of hukou conversion. Considering administrative dimension, socio-economic dimension, and environmental dimension, this study focuses on migrants with settlement intention and examines the impact of administrative level, city tier, and air quality on their willingness of hukou conversion, using questionnaire data collected in 334 cities in 2017. It is found that migrants with settlement intention have a strong incentive to obtain local hukou in destination cities with high administrative levels and city tiers in the northeast, east, and middle China. This effect is increasingly significant for destinations from southwest to northeast China. While in cities with lower city tiers, migrants with settlement intention are less willing to obtain local hukou in the destination. Air quality has a limited impact on migrants’ willingness of hukou conversion. But the role of air quality varies among different regions. These findings support that both environmental protection and economic development are important to promote urbanization in China.

Modeling human activity dynamics is important for many application domains. However, there are problems inherent in modeling population information, since the number of people inside a given area can change dynamically over time. Here, a cyberGIS-enabled spatiotemporal population model is developed by combining Twitter data with urban infrastructure registry data to estimate human activity dynamics. This model is an object-class oriented space–time composite model, in which real-world phenomena are modeled as spatiotemporal objects, and people can move from one object to another over time. In this research, all spatiotemporal objects are aggregated into 14 spatiotemporal object classes, and all objects in a given space at different times can be projected down to a spatial plane to generate a common spatiotemporal map. A temporal weight matrix is derived from Twitter activity curves for each spatiotemporal object class and represents population dynamics for each object class at different hours of a day. Finally, model performance is evaluated by using a comparison to registered census data. This spatiotemporal human activity dynamics model was developed in a cyberGIS computing environment, which enables computational and data intensive problem solving. The results of this research can be used to support spatial decision-making in various application areas such as disaster management where population dynamics plays an important role.

Sustainable cities are quintessential complex systems—dynamically changing environments and developed through a multitude of individual and collective decisions from the bottom up to the top down. As such, they are full of contestations, conflicts, and contingencies that are not easily captured, steered, and predicted respectively. In short, they are characterized by wicked problems. Therefore, they are increasingly embracing and leveraging what smart cities have to offer as to big data technologies and their novel applications in a bid to effectively tackle the complexities they inherently embody and to monitor, evaluate, and improve their performance with respect to sustainability—under what has been termed “data-driven smart sustainable cities.” This paper analyzes and discusses the enabling role and innovative potential of urban computing and intelligence in the strategic, short-term, and joined-up planning of data-driven smart sustainable cities of the future. Further, it devises an innovative framework for urban intelligence and planning functions as an advanced form of decision support. This study expands on prior work done to develop a novel model for data-driven smart sustainable cities of the future. I argue that the fast-flowing torrent of urban data, coupled with its analytical power, is of crucial importance to the effective planning and efficient design of this integrated model of urbanism. This is enabled by the kind of data-driven and model-driven decision support systems associated with urban computing and intelligence. The novelty of the proposed framework lies in its essential technological and scientific components and the way in which these are coordinated and integrated given their clear synergies to enable urban intelligence and planning functions. These utilize, integrate, and harness complexity science, urban complexity theories, sustainability science, urban sustainability theories, urban science, data science, and data-intensive science in order to fashion powerful new forms of simulation models and optimization methods. These in turn generate optimal designs and solutions that improve sustainability, efficiency, resilience, equity, and life quality. This study contributes to understanding and highlighting the value of big data in regard to the planning and design of sustainable cities of the future.

Gauging viral transmission through human mobility in order to contain the COVID-19 pandemic has been a hot topic in academic studies and evidence-based policy-making. Although it is widely accepted that there is a strong positive correlation between the transmission of the coronavirus and the mobility of the general public, there are limitations to existing studies on this topic. For example, using digital proxies of mobile devices/apps may only partially reflect the movement of individuals; using the mobility of the general public and not COVID-19 patients in particular, or only using places where patients were diagnosed to study the spread of the virus may not be accurate; existing studies have focused on either the regional or national spread of COVID-19, and not the spread at the city level; and there are no systematic approaches for understanding the stages of transmission to facilitate the policy-making to contain the spread.

To address these issues, we have developed a new methodological framework for COVID-19 transmission analysis based upon individual patients’ trajectory data. By using innovative space–time analytics, this framework reveals the spatiotemporal patterns of patients’ mobility and the transmission stages of COVID-19 from Wuhan to the rest of China at finer spatial and temporal scales. It can improve our understanding of the interaction of mobility and transmission, identifying the risk of spreading in small and medium-sized cities that have been neglected in existing studies. This demonstrates the effectiveness of the proposed framework and its policy implications to contain the COVID-19 pandemic.

Accessibility is a topic of interest to multiple disciplines for a long time. In the last decade, the increasing availability of data may have exceeded the development of accessibility modeling approaches, resulting in a modeling gap. In part, this modeling gap may have resulted from the differences needed for single versus multimodal opportunities for access to services. With a focus on large volumes of transportation data, a new measurement approach, called Urban Accessibility Relative Index (UARI), was developed for the integration of multi-mode transportation big data, including taxi, bus, and subway, to quantify, visualize and understand the spatiotemporal patterns of accessibility in urban areas. Using New York City (NYC) as the case study, this paper applies the UARI to the NYC data at a 500-m spatial resolution and an hourly temporal resolution. These high spatiotemporal resolution UARI maps enable us to measure, visualize, and compare the variability of transportation service accessibility in NYC across space and time. Results demonstrate that subways have a higher impact on public transit accessibility than bus services. Also, the UARI is greatly affected by diurnal variability of public transit service.

Climate vulnerability is higher in coastal regions. Communities can largely reduce their hazard vulnerabilities and increase their social resilience through design and planning, which could put cities on a trajectory for long-term stability. However, the silos within the design and planning communities and the gap between research and practice have made it difficult to achieve the goal for a flood resilient environment. Therefore, this paper suggests an AI (Artificial Intelligence)-driven platform to facilitate the flood resilience design and planning. This platform, with the active engagement of local residents, experts, policy makers, and practitioners, will break the aforementioned silos and close the knowledge gaps, which ultimately increases public awareness, improves collaboration effectiveness, and achieves the best design and planning outcomes. We suggest a holistic and integrated approach, bringing multiple disciplines (architectural design, landscape architecture, urban planning, geography, and computer science), and examining the pressing resilient issues at the macro, meso, and micro scales.

Sustainable urban design or planning is not a LEGO-like assembly of prefabricated elements, but an embryo-like growth with persistent differentiation and adaptation towards a coherent whole. The coherent whole has a striking character – called living structure – that consists of far more small substructures than large ones. To detect the living structure, this paper develops a new approach for uncovering the underlying living structure of urban environments. The approach takes an urban environment as a whole and recursively decomposes it into meaningful subwholes at different levels of hierarchy (or scale) ranging from the largest to the smallest. This approach helps us not only better understand an urban environment as a living structure, but also better plan or transform the urban environment to be living or more living, or equivalently to be beautiful or more beautiful.

Evidence shows that adolescents do not do enough physical activity (PA), which could contribute to childhood overweight and obesity. Studies have shown that both the built environment and social networks could influence adolescents’ PA, but more studies are needed to investigate their combined influence using longitudinal data. We used a stochastic actor-based model analyzing two waves of Add Health data to test if (1) home location has a significant influence on high school student’s friendships, and (2) the neighborhood built environment has a significant influence on high school student’s PA while controlling for friendship networks. The results indicate that students’ PA level emulated peers’ PA levels and students who lived closer together, increased the likelihood of forming friendships. However, the built environment variables that described adolescents’ residential neighborhoods did not show a significant influence on students’ PA dynamics. This study contributes to our understanding of the joint impacts of social networks and home location on adolescents’ friend networks and PA dynamics in urban settings.

Culture and distance are two major factors for geographically segmenting tourists in tourism marketing and advertising. Previous empirical studies on the destination image, however, have examined extensively the effect of the culture while inadequately the effect of the distance, let alone comparing the effects of the two variables. Using social media data, this study compares the effect of distance-based segments of tourists with that of culture-based segments in producing diverse perceived images of a destination. From Sina Weibo data, 282,532 Chinese mainland tourists who visited Suzhou, China during 2012–2016 and their perceived destination images are extracted and analyzed. The main results include: 1) for distance-based segments, the image differences increased with distance and the short-haul tourists perceived a more comprehensive image than the long-haul tourists; 2) for culture-based segments, the image differences were clear and relatively complex, while tourists from Wuyue cultural region had similar image perceptions with the local visitors; 3) the q-statistic of the Geodetector method shows that the culture-based segmentation can explain 65.8% of image variations while the distance-based segmentation can explain 46.6% of image variations, suggesting that culture is a more appropriate variable to segment the tourism market.

Most cities in the United States rely on zoning to address important planning-related issues within their jurisdictions. Planners often use GIS tools to analyze these issues in a spatial context. ESRI’s ArcGIS Urban software seeks to provide the planning profession with a GIS-based solution for various challenges, including zoning’s impacts on the built environment and housing capacity.

This research explores the use of ArcGIS Urban for assessing the existing zoning and comprehensive plans in meeting the projected residential growth in the near future using the City of Arlington, Texas as a case study. The exploration provides examples and lessons for how ArcGIS Urban might be used by planners to accomplish their tasks and highlights the capabilities and limitations of ArcGIS Urban in its current stand. The paper is concluded with some suggestions for future studies.

Auckland experienced phenomenal expansion since 1841. This study assesses the pace of urban sprawl and its control over the natural environment and housing affordability. After the urban built-up area was mapped, its change over time was detected, and correlated with population. From 1842 to 2014 built-up area in Auckland grew from 48 ha to 50,531 ha. The pace of growth was 151 ha/year during 1842–1945 but jumped to 989 ha per annum during 1975–2001. It dropped to only 249 ha per annum in this century. This unchecked sprawl is a direct response to population growth and facilitated by improved transportation. Since the late 1990s urban built-up areas experienced a subdued expansion despite continued population growth. This curtailed sprawl is attributed to the contentious planning regulations implemented to curtail sprawl. Consequently, population density rose to 28 persons/ha, the highest since a century ago. Urban growth has reduced biomass and green fields with mean vegetation index dropped from 129.5 to 118.7 during 2002–2014 with a smaller standard deviation, suggesting the landscape is increasingly homogenized. House prices rose slowly when the growth potential decreased slowly and vice versa (r = − 0.925) while the number of vacant sections suitable for single dwellings declined. Thus, controlled urban sprawl is largely responsible for the skyrocketed price of sections and declined housing affordability.

Advances in positioning and communicating technologies make it possible to collect large volumes of taxi trajectory data, quickly providing a complete picture of the ground traffic systems and thus being applied to different fields. However, there are still challenges for data users to handle such big data. In view of this, we have developed a software system named XStar to deal with trajectory big data. Its core is a scalable index and storage structure. Based on it, raw data can be saved in a more compact scheme and accessed more efficiently. A real taxi trajectory dataset is employed to demonstrate its performance. In general, XStar facilitates processing and analyzing trajectory data affordably and straightforwardly. Since its release in Jan. 2019, it has received downloads of over 4000 by May 2021. More analytical functions are being developed.

Navigation services utilized by autonomous vehicles or ordinary users require the availability of detailed information about road-related objects and their geolocations, especially at road intersections. However, these road intersections are generally represented as point elements without detailed information, or are even not available in current versions of crowdsourced mapping databases including OpenStreetMap (OSM). This study proposes an approach to automatically detect road objects from street-level images and place them to correct locations according to urban rules. Our processing pipeline relies on two convolutional neural networks: the first one segments the images, while the second one detects and classifies the specific objects. Moreover, to locate the detected objects, we propose an attributed topological binary tree (ATBT) based on urban rules for each image in an image sequence to depict the coherent relations of topologies, attributes and semantics of the road objects. Then the ATBT is further matched with map features on OSM to determine the right placed location. The proposed method has been applied to a case study in Berlin, Germany. We validate the effectiveness of the proposed method on two object classes: traffic signs and traffic lights. Experimental results demonstrate that the proposed app roach provides promising results in terms of completeness and positional accuracy.

This article presents a hybrid algorithm for the service area problem. The design of service areas is one of the essential issues in providing efficient services in both the public and private sectors. For a geographical region with a number of small spatial units, the service area problem is to assign the service-demand units to the service-supply units such that each facility has a service area. The basic criteria for the service areas are the highest service accessibility, the contiguous service areas, and that the service demand does not exceed the service supply in each service area. A hybrid algorithm for the service area problem is proposed by extending iterative local search (ILS) algorithm with three schemes: population-based ILS, variable neighborhood descent (VND) search, and set partitioning. The performance of the algorithm was tested using 60 well-designed instances. Experimentation showed that the instances could be solved effectively and efficiently. The solutions found by the hybrid algorithm approximate optimal solutions or the lower bounds with an average gap of 0.15%.

This study applies space syntax methodology and investigates the centrality process of four centers in Qingdao, China, which grow from old settlements during successive phases of urban growth. The aim is to increase the understanding of how urban form can generate and sustain centers as well as embed them in, or distinguish them from their context that has been built up in a rather complex geographic region. Results reveal the development of these centers and the associated different scales of accessibility are related to both pre-urban road network of the city and the local grid conditions of those settlements from which a center has grown. The overall condition of the city frames the global structure of the city and renders those settlements with potentials for movement-engaged activities. The local grid structure set by metric and topo-geometric properties influences the actual concentration patterns of social economic activities and determines the range and strength of a center in the city. The study suggests that the morphological structure of the city may be historical in nature, in the sense that old settlements are not simply “absorbed” by urban growth, but can sustain or even function as a center given proper spatial environment.

Walk-sharing is a cost-effective and proactive approach that promises to improve pedestrian safety and has been shown to be technically (theoretically) viable. Yet, the practical viability of walk-sharing is largely dependent on community acceptance, which has not, until now, been explored. Gaining useful insights on the community’s spatio-temporal and social preferences in regard to walk-sharing will ensure the establishment of practical viability of walk-sharing in a real-world urban scenario. We aim to derive practical viability using defined performance metrics (waiting time, detour distance, walk-alone distance and matching rate) and by investigating the effectiveness of walk-sharing in terms of its major objective of improving pedestrian safety and safety perception. We make use of the results from a web-based survey on the public perception on our proposed walk-sharing scheme. Findings are fed into an existing agent-based walk-sharing model to investigate the performance of walk-sharing and deduce its practical viability in urban scenarios.

Effectively monitoring the dynamics of human mobility is of great importance in urban management, especially during the COVID-19 pandemic. Traditionally, the human mobility data is collected by roadside sensors, which have limited spatial coverage and are insufficient in large-scale studies. With the maturing of mobile sensing and Internet of Things (IoT) technologies, various crowdsourced data sources are emerging, paving the way for monitoring and characterizing human mobility during the pandemic. This paper presents the authors’ opinions on three types of emerging mobility data sources, including mobile device data, social media data, and connected vehicle data. We first introduce each data source’s main features and summarize their current applications within the context of tracking mobility dynamics during the COVID-19 pandemic. Then, we discuss the challenges associated with using these data sources. Based on the authors’ research experience, we argue that data uncertainty, big data processing problems, data privacy, and theory-guided data analytics are the most common challenges in using these emerging mobility data sources. Last, we share experiences and opinions on potential solutions to address these challenges and possible research directions associated with acquiring, discovering, managing, and analyzing big mobility data.

United Nations’ Sustainable Development Goal targets to make cities and human settlements inclusive, safe, resilient, and sustainable; as it is predicting 95% urban expansion in the next decades. Consequently, urban livability can serve as a useful conceptual and analytical framework to improve the quality of urban life by facilitating the evaluation of the person–environment relationship and leading the improvement without deteriorating the environmental conditions. This present paper aims to identify the dimensions and indicators of subjective and objective livability for Siliguri Municipal Corporation (SMC). The residents’ perception has been carried out using stratified random sampling technique. Samples have been collected from the residents from each core, semi-periphery and peripheral areas of SMC. Mainly, adaptation of Exploratory Factor Analysis (EFA) and Confirmatory Factor Analysis (CFA) model involves four livability dimensions; under which the overall model explains 65% of the total variance indicating with the high reliability (α > 0.7) and the Goodness-of-fit index (GFI) about 0.90. The result indicates that, ‘Accessibility Factor’ bears the highest impact (24.91%) among the four latent variables and ‘Socio-Economic’ factor has the lower impact (8.39%) upon the urban livability.

The COVID-19 pandemic has caused various impacts on people’s lives, while changes in people’s lives have shown mixed effects on mitigating the spread of the SARS-CoV-2 virus. Understanding how to capture such two-way interactions is crucial, not only to control the pandemic but also to support post-pandemic urban recovery policies. As suggested by the life-oriented approach, the above interactions exist with respect to a variety of life domains, which form a complex behavior system. Through a review of the literature, this paper first points out inconsistent evidence about behavioral factors affecting the spread of COVID-19, and then argues that existing studies on the impacts of COVID-19 on people’s lives have ignored behavioral co-changes in multiple life domains. Furthermore, selected uncertain trends of people’s lives for the post-pandemic recovery are described. Finally, this paper concludes with a summary about “what should be computed?” in Computational Urban Science with respect to how to catch up with delays in the SDGs caused by the COVID-19 pandemic, how to address digital divides and dilemmas of e-society, how to capture behavioral co-changes during the post-pandemic recovery process, and how to better manage post-pandemic recovery policymaking processes.

The urban population is predicted to reach a 70% share of global population by mid-century. Future urbanization might be directed along several development typologies, e.g. sprawling urbanization, more compact cities, greener cities, or a combination of different typologies. These developments induce urban land-use change that will affect urban climate and might reinforce phenomena such as the urban heat island and thermal discomfort of urban residents. A planning-based mitigation approach to ensure thermal comfort of residents are urban cold-air paths, i.e. low-roughness areas enabling drainage and transport of colder air masses from rural surroundings. We study how urban land-use change scenarios influence cold-air path occurrence probability and spatial distribution in a mid-European city using a machine learning approach, i.e. boosted regression trees. The Urban Sprawl Scenario results in the strongest reduction of cold-air path area by 3.6% in comparison to the reference case. The Green City Scenario gives evidence for an increase of cold-air path area (2.2%) whereas the Compact Green City Scenario partly counteracts the negative influence of urban densification by increased fractions of vegetated areas. The proposed method allows for the identification of priority areas for cold-air path preservation in urban planning.

Ongoing efforts among cities to reinvigorate streets have encouraged innovations in using smart data to understand pedestrian activities. Empowered by advanced algorithms and computation power, data from smartphone applications, GPS devices, video cameras, and other forms of sensors can help better understand and promote street life and pedestrian activities. Through adopting a pedestrian-oriented and place-based approach, this paper reviews the major environmental components, pedestrian behavior, and sources of smart data in advancing this field of computational urban science. Responding to the identified research gap, a case study that hybridizes different smart data to understand pedestrian jaywalking as a reflection of urban spaces that need further improvement is presented. Finally, some major research challenges and directions are also highlighted.

Although studies have previously investigated the spatial factors of COVID-19, most of them were conducted at a low resolution and chose to limit their study areas to high-density urbanized regions. Hence, this study aims to investigate the economic-demographic disparities in COVID-19 infections and their spatial-temporal patterns in areas with different population densities in the United States. In particular, we examined the relationships between demographic and economic factors and COVID-19 density using ordinary least squares, geographically weighted regression analyses, and random forest based on zip code-level data of four regions in the United States. Our results indicated that the demographic and economic disparities are significant. Moreover, several areas with disadvantaged groups were found to be at high risk of COVID19 infection, and their infection risk changed at different pandemic periods. The findings of this study can contribute to the planning of public health services, such as the adoption of smarter and comprehensive policies for allocating economic recovery resources and vaccines during a public health crisis.

Young people are the backbone of urban development and an important pillar of social stability. The growth of young floating population in China has given rise to urban land expansion. Understanding the urban life pattern of urban life for young people benefits rational and effective land expansion. In this article, we introduce food delivery data into the process of exploring behavioral patterns of urban youth in Hangzhou, Zhejiang Province, China. The dynamic time warping (DTW) distance-based k-medoids method is applied to explore the main activity areas and activity patterns of the urban youth population. The results indicate that many young people from Hangzhou work in Internet companies, and most of work hotspot areas are observed in high-tech parks. The existence of overtime work is proved. Combined with the housing price data in Hangzhou, it is found that young people consider both housing prices and education environment when choosing where to live. The analysis combined with road network data reflects the planning characteristics of the city, also looks into differences between the actual city functions and the planning map. The proposed methods can provide useful guidance and suggestions for city planning.