News

Research Projects led by LSGI Scholars Awarded near $12 Million Funding from RGC

2 outstanding collaborative projects led by SCRI scholars have been supported by the Research Grants Council (RGC), with the total funding amounting to near HK12 million. The project led by Prof. John Shi aims to address the impact of COVID-19, while the one led by Prof. Charles Wong focuses on carbon sequestration under climate change. Collaborative Research Fund (CRF) 2021/22 Project Project Coordinator Amount Awarded (HK$) Spatiotemporal Prediction and Real-time Early Warning of COVID-19 Onset Risk This project aims to develop the methods for predicting the onset risk of COVID-19 symptoms, like fever and cough, in space and time at a fine scale (i.e., within a city). To achieve high prediction accuracy, the methods will incorporate fine-scale transmissibility and other epidemiologic features of different SARS-CoV-2 variants, at the same time the impacts of fine-scale urban characteristics and social contacts on COVID-19 transmission. A mobile application system will be developed to deliver the risk predictions and send active real-time early warning of high-risk areas or routes to the public. The project is expected to support lower-cost and more effective long-term control of COVID-19 and potential future epidemics. Prof. John Shi 6,964,000 Study of Carbon Sequestration in Hong Kong’s Vegetation: from Present to Future Prediction under Climate Change Carbon stock and carbon sequestration of vegetation play a pivotal role to absorb atmospheric carbon dioxide. For a low carbon economy under the Government’s Hong Kong’s Climate Action Plan 2030+, study on carbon storage capacity of local vegetation and sequestration rates for rural and urban areas becomes crucial. The project proposes to use geospatial technologies to map and estimate the biomass and carbon sequestration of Hong Kong’s vegetation, by integrating satellite-, airborne-, and ground-based remote sensing technologies. Prof. Charles Wong 4,949,639 Administered by RGC, the CRF supports investigators across disciplines and/or across universities to engage in creative and multi-disciplinary research projects, while the RIF aims to foster impactful and translational collaborative research beyond academia for the benefit of wider community.

SCRI predicts five more districts to face high Omicron risks

The research team led by Prof. Shi Wenzhong, Director of the Otto Poon Charitable Foundation Smart Cities Research Institute predicted the spatiotemporal spread of Omicron in the Hong Kong community by big data, using the locations of patients, time of their diagnosis and developing symptoms, population mobility, vaccination rate, and social distance index to figure out high-risk areas. The average risk of being infected with Omicron in Hong Kong will increase in the next week, with high-risk districts rising to 11 from the current six, to include areas such as Sha Tin and Kowloon Bay. Online coverage (English) SCMP - https://polyu.me/3fcBgSb The Standard - https://polyu.me/3K2z8dZ Online coverage (Chinese) Now TV - https://polyu.me/3qfL58e  RTHK - https://polyu.me/3HK8YL8 Oriental Daily News - https://polyu.me/3GjOf0p  Hong Kong Economic Times - https://polyu.me/3tfJIZa  Sing Tao Daily - https://polyu.me/3FiXTix  am730 - https://polyu.me/3ndXjwc Sky Post - https://polyu.me/3f9qxYH  Wen Wei Po - https://polyu.me/3IcBAgr Hong Kong Commercial Daily - https://polyu.me/3zLt7h6  HK01 - https://polyu.me/33mrKJw

SCRI Research: Predicts Omicron onset risk in Hong Kong and finds Omicron spread can be better controlled by strict and targeted measures and booster vaccination

Professor John Shi, Director of SCRI, PolyU has proposed an extended Weight Kernel Density Estimation (WKDE) model to predict the spatiotemporal risk of COVID-19 symptom onset. Now this model has been adapted to understand how to track and control the spatiotemporal spread of Omicron variant. Prediction of Onset Risk in Hong Kong The prediction results show that from 11 to 17 January 2022, the overall risk of Omicron in Hong Kong continued to increase, and the growth rate was slower than that in South Africa. High-risk areas include: North Point, Kowloon Tong, Tuen Mun, Tai Po, Causeway Bay, Yau Tsim Mong, Tsuen Wan, Tseung Kwan O, etc. The deployment of vaccines and testing resources should be strengthened for these areas, and high-risk areas are concentrated in densely populated and high-traffic areas. The team calls on citizens to temporarily reduce non-essential travel in these areas. In addition, the forecast confirms that the recent tightening of social distancing measures by the government and the reduction in travel of Hong Kong citizens (according to Apple Mobility Trends Reports) have played a positive role in slowing the spatiotemporal spread of Omicron. Fig. 1. (a) The symptom onset risk prediction in Hong Kong from 11 to 17 January 2022. (b) The predicted onset risk with and without the tighting of social distancing measures from 7 January 2022. Omicron spread can be better controlled by strict and targeted measures and booster vaccination Summary. A study led by Prof. Wenzhong Shi from the Otto Poon Charitable Foundation Smart Cities Research Institute (SCRI) at The Hong Kong Polytechnic University (PolyU) provides the first information on how to track and control the spatiotemporal spread of SARS-CoV-2 Omicron variant in South Africa. It is found that compared with current Alert Levels 1-4 in all provinces, the imposition of lockdown in the high-onset-risk Gauteng together with the Alert Level 1 in other provinces has a higher potential to effectively control the spread of Omicron and could reduce the overall symptom onset risk by up to 15.34% from 26 Nov 2021 to 10 Dec 2021. Meanwhile, if the current daily vaccination speed in each province increased by 10 times, the daily overall onset risk was estimated to reduce by only up to 7.86% from 26 Nov 2021 to 10 Dec 2021. This highlights the necessity to vaccinate the 1.6 million people in South Africa who have been fully vaccinated over 6 months ago with the booster dose of vaccine as soon as possible. This research is currently under peer review for publication. [Research article about this study] Method and findings. The province-level WKDE model made the onset risk prediction based on the following data: i) Daily human mobility, ii) time-varying vaccination rates and vaccination efficiency, iii) daily COVID-19 effective reproductive number R, iv) weekly respiratory pathogens surveillance reports, v) weekly levels of SARS-CoV-2 in wastewater treatment plants, vi) weekly COVID-19 cases admitted to sentinel hospital surveillance sites, vii) the weekly percentage testing positive, and viii) daily social distancing level at the province scale. The model achieved over 80% accuracy in the onset risk prediction for the following 7 days. Based on the onset risk prediction results, the actual spatiotemporal spread of Omicron in South Africa was analyzed, and the Omicron spread under different scenarios with different epidemic alert levels and vaccination rate levels was further simulated. It was found that: i) The spatiotemporal spread was relatively slow during the first stage and following the emergence of Omicron in Gauteng. The spatial spread of Omicron accelerated after it had become the dominant variant, and continued to spread from Gauteng to the neighboring provinces and main transport nodes. ii) Compared with current Alert Levels 1-4 in all provinces, the imposition of Alert Level 5 (lockdown) in the high-onset-risk Gauteng together with Alert Level 1 in other 8 provinces had a higher potential to effectively control the spread of Omicron. This recommended strict and targeted measure was estimated to be able to reduce the overall symptom onset risk by up to 15.34% from 26 Nov 2021 to 10 Dec 2021, and, moreover, it can reduce the spread of the Omicron epidemic in the provinces where main international airports are located to other parts of the world. iii) Due to declining vaccine efficiency over time, even if the daily vaccination rates in each province increased by 10 times since 26 Nov 2021, the daily overall onset risk was only reduced by 0.34%-7.86% by 10 Dec 2021. ‘It is important to effectively control the human mobility from high-onset-risk area to other areas by lockdown in high-risk areas,’ said Prof. Shi. ‘It is also noted that, by implementing general gathering control measures in other areas, the impact on social and economic activities in other regions can be reduced. Boosters need to be vaccinated to strengthen the control of the Omicron epidemic. In sum, our study shows that the Omicron outbreaks could be better controlled through targeted strict measures and the booster vaccination.’    (a) (b) Fig. 2. The risk of COVID-19 symptom onset in the five epidemic alert scenarios (i.e., the current Alert Level 1 in all 9 provinces, Alert Level 2 in all 9 provinces, Alert Level 3 in all 9 provinces, Alert Level 4 in all 9 provinces, and Alert Level 5 in Gauteng together with Alert Level 1 in other 8 provinces) from November 25th, 2021 to December 10th, 2021. (a) (b) Fig. 3. The risk of COVID-19 symptom onset with the current daily vaccination rates, 5 times the vaccination rates, 10 times the vaccination rate at the Alert Level 5 for Gauteng together with Alert Level 1 for the remaining 8 provinces from November 26th, 2021 to December 10th, 2021.   Related works: spatial prediction of COVID-19 onset risk Predicting the risk of COVID-19 is the key to combating the pandemic worldwide. Most existing COVID-19 risk prediction methods focus on confirmed cases. However, people are more infectious in the days around and following the symptom onset (e.g., fever or cough), and there is a spatially variant delay of 4~5 days on average from the onset to diagnosis. Therefore, predicting the spatiotemporal risk of COVID-19 onset, which is different from the risk in terms of confirm cases, is essential for timely anti-epidemic measures. The team of Prof. Shi developed an extended WKDE model to predict the spatiotemporal risk of COVID-19 onset within 14 days. The model was used for evaluating the effect of Wuhan lockdown in reducing the COVID-19 risk in other cities in China. The lockdown was found to delay the arrival of the COVID-19 onset risk peak for 1–2 days and lower risk peak values in other Chinese cities. The decrease of onset risk was more than 8% in over 40% of Chinese cities, and was up to 21.3%. Lockdown was the most effective in areas with medium onset risk before the lockdown. [Research article about this study] The extended WKDE model was further developed to predicting the onset risk within Hong Kong. Based on the prediction result of the model, a spatial and dynamic solution based on the community-scale COVID-19 onset risk prediction result was also developed for precisely allocating COVID-19 vaccines to different areas and population groups in Hong Kong. [Research article about this study] A further improved model was also used to evaluate the anti-epidemic measure in Taiwan. When the COVID-19 spread in Taiwan rebounded in May 2021, the epidemic alert in entire Taiwan rose to Level 3 (closing business places and public venues). However, the study found that compared with Level 3 Alert in entire Taiwan, Level 4 Alert (lockdown) in Taipei and New Taipei with the highest onset risk and Level 2 Alert in the rest of Taiwan (re-open venues, gather control) can better control the epidemic and reduce onset risk of up to 91.36%. Also, Increasing the daily vaccination rate in each district by up to 5 to 10 times would further reduce the onset risk by 6.07% to 62.22%. [Research article about this study] Acknowledgements This study was supported by Otto Poon Charitable Foundation Smart Cities Research Institute, The Hong Kong Polytechnic University (Work Program: CD03), and National Key R&D Program of China (2019YFB2103102). About the Smart Cities Research Institute (SCRI), PolyU  Otto Poon Charitable Foundation Smart Cities Research Institute (SCRI) provides an interdisciplinary platform for PolyU’s experts to develop an international leading area in Smart Cities by capitalizing on existing interdisciplinary research strengths, including but not limited to departments of Faculty of Construction and Environment, Faculty of Engineering, Faculty of Applied Science and Textiles, and Faculty of Business with including other research institutes. To respond to the current gap of the unique traffic characteristics of Hong Kong, the SCRI first initiates a pilot research on smart mobility. As an internationally leading center of excellence in smart mobility, the proposed research framework aims to develop a three-year strategic plan, including four research initiatives.  Media enquiries Please contact Prof. John Shi via lswzshi@polyu.edu.hk .  

Naming Ceremony of the Otto Poon Charitable Foundation Smart Cities Research Institute and Otto Poon Charitable Foundation Research Institute for Smart Energy

Thanks to a generous donation from the Otto Poon Charitable Foundation (the Foundation), The Hong Kong Polytechnic University (PolyU) has established two research institutes in support of the University’s research endeavours in the areas of smart cities and sustainable energy. The two research institutes are named the Otto Poon Charitable Foundation Smart Cities Research Institute (SCRI) and the Otto Poon Charitable Foundation Research Institute for Smart Energy (RISE) in appreciation of the Foundation’s significant contributions. Officiated by Dr David Chung, Under Secretary for Innovation and Technology of the HKSAR Government, the naming ceremony was held earlier this month. It was attended by Ir Dr Otto Poon Lok-to, Founder of the Otto Poon Charitable Foundation, Dr Lam Tai-fai, PolyU’s Council Chairman, Professor Jin-Guang Teng, PolyU’s President and others. In his welcoming remarks, PolyU’s Council Chairman Dr Lam Tai-fai extended his heartfelt gratitude to Ir Dr Otto Poon Lok-to for his long-standing support to PolyU, “Dr Poon is a strong advocate of using innovation and technology to solve societal problems. Smart cities and energy challenges are two topics of great importance to society today. We are excited to join hands with Dr Poon to set up two research institutes focusing on these strategic areas in order to contribute to the further development of Hong Kong, the Nation and the wider international community.” The Under Secretary for Innovation and Technology, Dr David Chung, said that the Government had been collaborating with the two Otto Poon Charitable Foundation Research Institutes in conducting trials and pilots in a number of smart city projects, ranging from the application of urban informatics to smart and sustainable energy, in order to improve the quality of life of our citizens. “The Innovation and Technology Bureau will continue to develop Hong Kong into a smart city and to make space for our local talents to help contribute to the betterment of Hong Kong as well as our country. I urge all of you to take advantage of the opportunities brought about by the National 14th Five-year Plan and development in the Guangdong-Hong Kong-Macao Greater Bay Area. There is much to gain from collaboration with our neighbour cities and we have much to learn from each other, especially on the new frontier of smart city development. PolyU, with its outstanding research institutes and academics, will have a big role to play in building Hong Kong into an international I&T hub,” Dr Chung added. Founder of the Otto Poon Charitable Foundation Ir Dr Otto Poon Lok-to remarked that “the notion of smart cities embraces a wide array of topics. The establishment of SCRI will serve as a catalyst to blend the various elements of smart cities together in order to contribute to the success of Hong Kong’s Smart City Blue Print 2.0.” Ir Dr Poon also raised concerns regarding climate change, and noted that renewable energy and energy storage were two essential research topics. He appreciated PolyU researchers' dedicated commitment to advancing the frontiers of technology and knowledge to cope with energy challenges. Professor Jin-Guang Teng, President of PolyU, thanked Ir Dr Poon for his unwavering support to PolyU over the years and said “Interdisciplinary collaboration can provide solutions to address societal challenges. Against this backdrop, the University has established the PolyU Academy for Interdisciplinary Research (PAIR), a hub to promote research and innovation across disciplines. To date, ten research institutes and five research centres have been established to offer impactful solutions in areas including land and space creation, smart ageing, advanced manufacturing, smart cities, smart energy and more.” Established in 2020, SCRI and RISE will bring together PolyU experts from diversified fields to develop impactful interdisciplinary research. SCRI aims at being a global centre of excellence in urban informatics and a living smart cities laboratory for Hong Kong and the Guangdong-Hong Kong-Macao Greater Bay Area in order to promote smart cities development in Hong Kong and in the country. Its research focus areas include Smart Mobility, Smart Living, Smart Environment, Smart People, Smart Government, and Smart Economy. SCRI is collaborating with some of the world’s top universities including the University of Cambridge and University College London, as well as major industrial players to develop innovative solutions. SCRI’s innovations have received worldwide recognition and won two Gold Medals at 2021 Inventions Geneva Evaluation Days and two prizes in the 2021 Smart 50 Awards. To see some happy moments at the ceremony, please click here. For more details about the research institutes, please visit the respective websites: Otto Poon Charitable Foundation Smart Cities Research Institute (SCRI) https://www.polyu.edu.hk/scri/ Otto Poon Charitable Foundation Research Institute for Smart Energy (RISE) https://www.polyu.edu.hk/rise/

SCRI Research: Big Data for Smart Tourism

The initiative aims to couple different sources of tourism big data to obtain an improved understanding of tourist activity patterns in and cross cities. The insights will be further used to support local and regional tourism planning, and develop tourism recommendation systems for domestic and international travelers. For more information, please visit https://yangxu-git.github.io, or contact Dr. Yang Xu via yang.ls.xu@polyu.edu.hk.   Characterizing destination networks through mobility traces of international tourists We demonstrate how large-scale tourist mobility data can be linked with network science approaches to better understand tourism destinations and their interactions. By analyzing a mobile positioning dataset that captures the nationality and movement patterns of foreign tourists to South Korea, we employ a few metrics to quantify the network properties of tourism destinations, aiming to reveal the collective dynamics of tourist movements and key differences across nationalities.   Measure inter-city tourist flows Quantify destination attractiveness Preferences across nationalities “Travel Communities” extracted from inter-city tourist movements   Travel Recommendation System Leveraging navigation data, vehicle trajectories, consumer data and Point of Interest (POI), we have developed algorithms as proof of concept for travel recommendation system for Jeju, South Korea. Tourism movement patterns on weekdays vs. weekends Location recommendation Activity recommendation   Understanding tourist time use By using a large-scale mobile phone data set collected in three cities in South Korea (Gangneung, Jeonju, and Chuncheon), we develop a computational framework to enable accurate quantification of tourist time use, the visualization of their spatiotemporal activity patterns, and systematic comparisons across cities. The framework consists of several approaches for the extraction and semantic labeling of tourist activities, visual-analytic tools (time use diagram, time–activity diagram) for examining their time use, as well as quantitative measures that facilitate day-to-day comparisons. Daily time-use diagram of travelers in cities Spatial patterns of tourist activities Acknowledgement: School of Hotel and Tourism Management, The Hong Kong Polytechnic University Jeju Special Self-Governing Province Korea Tourism Organization Jeju Tourism Organization   Publications: [1] Xu, Y., Li, J., Xue, J., Park, S. and Li, Q., 2021. Tourism Geography through the Lens of Time Use: A Computational Framework Using Fine-Grained Mobile Phone Data. Annals of the American Association of Geographers (in press) [2] Xu, Y., Xue, J., Park, S. and Yue, Y., 2021. Towards a multidimensional view of tourist mobility patterns in cities: A mobile phone data perspective. Computers, Environment and Urban Systems, 86, p.101593. [3] Xu, Y., Li, J., Belyi, A. and Park, S., 2021. Characterizing destination networks through mobility traces of international tourists—A case study using a nationwide mobile positioning dataset. Tourism Management, 82, p.104195. [4] Park, S., Xu, Y., Jiang, L., Chen, Z. and Huang, S., 2020. Spatial structures of tourism destinations: A trajectory data mining approach leveraging mobile big data. Annals of Tourism Research, 84, p.102973.

SCRI Scholars won 2021 Smart 50 Awards

In late August, Prof. Charles Wong and Prof. John Shi from SCRI were honoured with the 2021 Smart 50 Awards for their smart city projects. It is the first time that LSGI research team has seized the prestigious awards. The Smart 50 Awards recognize 50 of the most innovative and influential smart cities’ projects around the world, in partnership with Smart Cities Connect, Smart Cities Connect Foundation and US Ignite. The award presentation ceremony will be held in the Untied States in late 2021.    Developed by Prof. Charles Wong Man-sing and his fellow researchers from the LSGI, the the first ever smart tree monitoring project wins the Smart 50 Awards this year. This smart management system makes use of wireless sensors and geographic information systems to predict and inspect titled trees with potential danger. The project also won a Gold Medal at the Special Edition 2021 Inventions Geneva Evaluation Days organised by the International Exhibition of Inventions of Geneva.    Another winner of the award, Prof. John Shi, led his team to develop a comprehensive spatial analysis and onset risk prediction platform which could trace the community spread of COVID-19. By analysing Hong Kong’s urban structure, transportation network, population environment and other socio-economic data using spatial big data technologies, an algorithm was developed to accurately predict the trend of the epidemic in a well-timed manner, supporting the Public Health Department to formulate more precise preventions and control strategies.   Congratulations to Prof. Charles Wong, Prof. John Shi and all their fellow researchers!

Press sharing on SCRI smart cities related research

On 26 April, a press conference was conducted at PolyU by Prof. John Shi, Chair Professor of LSGI and Director of Smart Cities Research Institute to share a number of cutting-edge patented technologies and research by his research group. These projects help address various societal issues, including the revitalisation of old buildings, slope safety, prevention and control of the COVID19 pandemic, and the construction of spatial data infrastructure, hoping to provide comprehensive solutions for smart city development in Hong Kong and the Nation.   The project Smart Cities Research Platform received a Gold Medal at this year’s Special Edition 2021 Invention Geneva Evaluation Days- Virtual Event.    Other research projects shared:  Three-dimensional (3D) Mobile Mapping System: Providing accurate 3D maps to support wide smart city applications AI-based Landslide Recognition: Reporting landslide and facilitating disaster control Spatiotemporal Prediction of COVID-19 Onset Risk: To help public health agencies formulate more precise prevention and control strategies

SCRI Scholars Publish the Book Urban Informatics

The new book entitled Urban Informatics has been edited by Prof. Wenzhong Shi (Chair Professor of LSGI), Prof. Michael Goodchild (University of California, Santa Barbara), Prof. Michael Batty (University College London), Prof. Mei-Po Kwan (The Chinese University of Hong Kong) and Dr. Anshu Zhang (Research Assistant Professor of LSGI), was published on 8 April 2021. This is open access and a milestone book which systematically introduces the principles of emerging urban informatics and its wide applications in enabling cities to function more efficiently and equitably, to become ‘smart’ and ‘sustainable’.   The milestone book brings together over 140 authors from more than 40 world-leading research teams. The book is organized into five main parts: urban science, urban systems and applications, urban sensing, urban big data infrastructure, and urban computing. For each field which are covered under the five parts, the book offers a comprehensive review and a detailed technical introduction to the principles, methods and tools that form the core of urban informatics. It also outlines ways in which these methods and tools can be used to inform the design, policy, and management of urban services as well as ways in which cities can be planned to become more efficient with a greater concern for environment and equity.    The open access book can serve as a reference book for the researchers and professionals, as well as a textbook for relevant postgraduate and undergraduate courses.    Link for download: https://www.springer.com/gp/book/9789811589829 

SCRI scholars ranked as the world’s top 2% most-cited scientists by Stanford University

We are glad to share that four academic staff from SCRI are listed among the top 2 % scientists in the world according to a report by Stanford University in early 2021. The listed SCRI scholars are: Prof. John Shi, Chair Professor of LSGI and Director of SCRI, Dr. Xiaolin Zhu, Assistant Professor of LSGI and Member of SCRI   The report was prepared by a research team led by Prof. John Ioannidis of Stanford University. According to the team, the publicly available database of scientists among the world that provides standardized data on citations, h-index, co-authorship adjusted hm-index, citations to papers in different authorship positions, and a composite indicator. The SCRI scholars have demonstrated substantial influence in various disciplines through the publication of multiple highly cited papers ranked according to citation impact in 2019. While Prof. Shi is also listed in the top 2% most-cited scientists for career-long impact from 1996 to 2019. Disciplinary differences in terms of citation norms are taken care of since the list identifies the top 2% of scientists in their own areas of specialty.  Congratulations to all of the listed scholars! 

SCRI Research: Logistics Big Data for Intra-Urban Goods Movement Patterns

This research proposes an analytical framework for exploring intra-urban goods movement patterns by integrating spatial analysis, network analysis and spatial interaction analysis. Using daily urban logistics big data (over 10 million orders) provided by the largest online logistics company in Hong Kong (GoGoVan) from 2014 to 2016, we analyzed two spatial characteristics (displacement and direction) of urban goods movement. The origin–destination flows of goods were used to build a spatially embedded network, revealing that Hong Kong became increasingly connected through intra-urban freight movement. Finally, spatial interaction characteristics were revealed using a fitting gravity model. Workflow of The Empirical Analysis   1. Spatial Characteristics of Goods Movement We observe that the probability distributions for datasets of different years displayed similar trends and could be well fitted by a bimodal Weibull distribution. Specifically, two maximum points exist, namely, 6 km and 22 km. This means that the count of goods movement did not decrease monotonously with distance between origin and destination. Interesting to note is that the intra-urban freight movement displacement distributions failed to follow an exponential or power law distribution. According to the depiction in subsection on the measurement of spatial characteristics, we can conclude that average freight distance gradually increased. We can also conjecture that increasingly improved transportation networks provided greater convenience for freight, especially long-distance freight. Displacement distributions in different years: (a) probability distributions for the datasets; (b) cumulative probability distributions for the datasets. Direction distributions over 3 years   2. Characteristics of the spatially embedded network of goods movement We constructed a spatially embedded network based on origin–destination flow between units and conducted network analysis. By comparing the network properties for the networks in the years 2014, 2015 and 2016, we observed that Hong Kong became increasingly connected from the perspective of logistics. Furthermore, we investigated the distributions of degree and strength of nodes and examined the correlation between them. We found that their relationships could be well fitted by exponential functions, and all values of goodness-of-fit R2 reached 0.72 or higher. In other words, the freight flows between subdistricts could be estimated by the connectivity of subdistricts at the aggregate level. Freight movement flows for network construction Cumulative probability distributions and correlations of degree and strength   3. Characteristics of Spatial interaction We explored the spatial interaction characteristics of intra-urban freight movement and how the interaction flows were related both to the population (or total trips) of the origin and destination and to the distance between subdistricts by fitting the gravity model. The significant linear relationships between interaction flow and the product of the subdistrict populationsPiPj were observed by fitting the general form of the gravity model. In addition, we found that gravity was suitable for predicting goods movement flows by comparing the estimated interaction flows and observed interaction flows. However, the distance decay parameterβ was significantly smaller than that of human mobility patterns. We concluded that the spatial interaction of goods movement was not substantially influenced by the distance between origin and destination.   Comparison of estimated interaction flows with the observed interaction flow based on the datasets for the years 2014, 2015 and 2016   REFERENCE Zhao, P., Liu, X., Shi, W., Jia, T., Li, W., & Chen, M. (2020). An empirical study on the intra-urban goods movement patterns using logistics big data. International Journal of Geographical Information Science, 34(6), 1089-1116. Contact: Dr. Xintao Liu (xintao.liu@polyu.edu.hk)