Current cohort: May 2024
Here are the typical sections included in a research proposal:
Title: A concise title that captures the essence of the research project.
Background information, Research gaps, Research questions or hypothesis, and Objectives or aims
for the research projects.
Background information:
It provides context for the study, including relevant literature, previous research, and the current state of knowledge in the field. It helps others understand why the research is important.Research gaps:
It refers to the research area or question where existing research has not adequately addressed or answered. Research gaps can arise for various reasons, such as limitations in previous studies (from literature), emerging trends or technologies and varying societal needs that have not been explored.Research questions or hypothesis:
It should clearly state the main research question or hypothesis that the study seeks to address.Objectives or aims:
It explains what the research aims to achieve or what specific concern you hope to solve and why these goals are important within the scope of the proposed study.Specifically, Table 1 lists all the important research elements for research proposals in urban analytics. Please incorporate all the elements in your research proposal and clearly deliver this table as Appdedix in the proposal.
Table 1 The checklist of the important elements in research proposal.
Element | Description | Example |
---|---|---|
Data type | Data types refer to the formats of information collected. In urban science, data can be categorised into various types for different intentions. For example, Crime data can be collected by crime survey data, policing recorded data, sel-report data and so on. Urban mobility data can be categorised into mobile phone call detail records, mobile phone GPS data, underground smart card data, WiFi data, social media data and so on. | Underground smart card data, policing recorded data |
Data resource | Data resources are the various sources you collected the data under different usage licences (e.g., education and research licenses). Please read the data use policy carefully if you get access to the open data. | London datastore |
Independents/features/predictors(X) | Features or independent variables are the attributes of the data that are used to predict the outcome. | Population mobility variables (measured by travel behaviours from smart card data) |
Dependents/targets/responses(y) | Target variable or dependent variable predict or understand based on the feature variables. For supervised learning tasks, y typically consists of the labels or responses (e.g., can be a column) associated with each set of feature variables in the dataset. | Theft counts |
Spatial unit of analysis | The spatial unit of analysis refers to the geographic level or scale in the research analysis. It defines the spatial resolution or granularity in understanding spatial patterns. Clearly defining the geographical unit of analysis can help to avoid the Ecological Fallacy in the research findings. |
Lower Super Output Area (LSOA) |
Temporal unit of analysis | Temporal unit of analysis refers to the time scale or interval in analysis, e.g., the examination of temporal patterns, trends, or relationships. It can categorised into hourly, daily, weekly, monthly or yearly. In some prediction tasks, it emphasises the predicting power in the time scale of the trained model, e.g., the model can predict the next week (week-level) for each LSOA. | Monthly |
Study area and city | It means the specific urban areas of some select cities as the case study in the analysis, e.g., City of London areas in Greater London. | All urban areas in Greater London |
Observation period | It means the temporal period of the observation in the experimental analysis, e.g., the observation period covers 2021 to 2022 (two years). | 2021 year |
Model/method | The main method/model will be used or trained for solving the research questions, such as some statistical models or machine learning models. | Random Forest regressor |
The starting date of the research project for this cohort is Jan 2025.
related topics: 1, 2
related topics: 1, 2
related topics: 3
related topics: 3
related topics: 3, 6
related topics: 5
related topics: 3, 4, 5, 6
related topics: 3, 4, 5, 6
related topics: 3
related topics: 4
related topics: 4
related topics: 4
related topics: 4
related topics: 5
related topics: 6
related topics: 3, 5, 6
related topics: 6
related topics: 6
Several tools can be employed for analyzing urban data, particularly focusing on geospatial and temporal data processing and modelling. Selected Python packages and software are provided for your reference:
It is highly recommended to utilise GitHub for project management and code writing with version control. Further guidance on GitHub usage can be found at Github Docs. You can also find some online courses at Linkedin Learning, Udemy or Coursera. The simplest method involves utilizing GitHub Desktop to commit and push your local Jupyter Notebook project.
Table A1 The information on current research topics
No | Title | Description |
---|---|---|
1 | Spatio-temporal prediction for urban crimes (Crime prediction) using machine learning/ deep learning and big data | This project aims to develop an advanced crime prediction or analysis framework/method by leveraging advanced machine learning and deep learning techniques in the context of spatial and temporal big data analytics. By integrating historical crime data with relevant socio-economic and environmental factors, the project seeks to enhance the accuracy and efficiency of crime prediction in space and time or focus on explaining the specific crime patterns detected (e.g., crime hotspots and concentration). Students are encouraged to incorporate urban big data to improve crime analysis appraoch. The implementation will be designed for existing law enforcement systems to enhance effective public safety. |
2 | Exploring the urban mobility patterns using big data analytics | Understanding and optimising mobility are crucial for sustainable and efficient modern urban development. The objective of this project is to explore the complex patterns in urban mobility by analysing diverse big data sources, such as public transportation records (e.g., smart card data), social media big data and mobile phone big data. Other tasks of this project can focus on identifying what key factors (urban facilities and functional land use) influence the population’s mobility patterns (e.g., commuting behaviours), or predicting the volume of the population’s mobility trends (e.g., origin and destination flows) across different urban areas. Employing alternative methods such as machine learning and geospatial analysis will be pivotal in extracting meaningful insights for urban planning or public resource management. |
3 | Urban transport analytics using big data | The project aims to analyse the heterogeneity in urban transportation demand, usage/ridership, or model choices across urban areas to understand the diverse patterns of mobility within urban settings. By employing advanced geospatial data analytics and machine learning techniques, different types of transport mode patterns (e.g., taxi, cycling, public transportation) can be sensed, visualised, and analysed from various geo big data, such as smart card data, bike-sharing docking station data and mobile phone data. |
4 | Evaluating urban vitality /vibrancy using geo-big data | This project aims to evaluate the urban vitality across retail areas (or high streets) through the footfall traffic sensed from geo-big data. The primary task of this project is to explore the daily rhythms of vitality (represented by footfall traffic at place venues) across different urban land use areas. Second, it seeks to identify key factors influencing the vitality, encompassing aspects such as economic revitalization, social cohesion, and environmental sustainability. To achieve this objective, alternative research methods, including spatial and temporal analyses, and machine learning models will be explored to provide a comprehensive understanding of the dynamics of urban areas. |
5 | Sensing urban functions through big data analysis | In the context of dynamic urban environments, this project endeavours to employ AI tools to sense and comprehend various urban functions from geo big data. Against the backdrop of rapidly evolving cities, understanding the intricate interplay of urban population interacting with diverse functions in the urban landscape is crucial for effective urban planning and management. The primary objective of this project is to detect and portray the dynamic urban function zones from human activity patterns sensed from geo big data (e.g., social media data, mobile phone data, smart card data, street view data and remote sensing data). |
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Contact: Tongxin.Chen@hull.ac.uk