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UAGP-Project Brief - heat pump uptake v1.2

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UAGP-Project Brief-Detecting different types of recreation from geo-tagged photos

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This project is interesting because:

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Interest:

The aspect of this topic that interests me lies in its strong correlation with physical space. Based on the knowledge I have, the thermal environment of a building involves numerous factors, such as the window-to-wall ratio, orientation, room layout, room depth, the insulation materials of the building, wall thickness, and many other aspects. These elements are particularly challenging to quantify accurately as indicators (since some physical conditions that may seem suboptimal could potentially be optimized through a designer’s efforts). At the same time, however, this requires us to incorporate socio-economic conditions into our considerations. I find the challenge of integrating these factors into a model both intriguing and exciting.

Require:

Firstly, Gather localized temperature data of the experimental subjects over a 5-week period using data from various temperature monitoring stations. Additionally, include environmental factors such as solar radiation, wind speed, and similar variables to provide a comprehensive dataset.

Secondly, Treat indoor temperature as the dependent variable and use Building Performance Simulation (BPS) modeling to analyze the impact of outdoor temperature and building-related factors (e.g., window-to-wall ratio, room layout, insulation materials) on indoor temperature.

Thirdly, Quantify the high-temperature risk for each building, such as by calculating the number of days or the proportion of time when temperatures exceed a certain threshold, to assess the risk levels under high-temperature conditions.

Finally, Perform clustering analysis based on socio-economic conditions and high-temperature risks. For example, categorize the data into groups like high-risk-high-income, high-risk-low-income, low-risk-high-income, and low-risk-low-income. Based on the analysis, propose recommendations for future building retrofitting and optimization plans.

Skills: Building Performance Simulations / Arcgis / R / SPSS / JMP

Knowledge:

The impact of buildings themselves on thermal effects (structure, materials, layout, window-to-wall ratio, etc.);

The collection and conversion of temperature data (whether the urban heat island effect needs to be considered and how meteorological station data can be extrapolated to estimate localized temperatures around buildings).

More:

If the experimental approach is correct, the main issues focus on how to operate BPS-related software; how to extrapolate point-based temperature data (currently, I can only think of methods like using contour lines, but I haven’t given much thought to urban heat island effects or certain factors between buildings). Additionally, I don’t fully understand what studying multidimensionality means. Does it refer to analyzing whether the factors influencing indoor temperature change across different time scales or building types?

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Interest: I am very interested in this research approach which involves using machine learning for image recognition and classification, utilizing location and time data to model users' travel paths and speeds, and eventually determining the points of interest (POIs) in parks along with the types of activities around these POIs, to suggest improvements for the parks.

Requirement: We probably don't need to collect more data. First, we should split into two teams: one handling the machine learning and the other responsible for simulating and visualizing the GPS data paths. Next, we might need to classify the photo data to identify several types of activity, compile these activity types into the space, and correlate them with the POIs in a manner similar to geographic regression: cluster analysis to determine the patterns between POIs and activity types.

Skills: GIS (time-space dataset, GWR) / Python (ML)

Knowledge: Using Python for machine learning, categorizing labels, organizing data; aligning data granularity; simulating users’ travel paths; deciding on classification standards for categorizing photos; how to classify POIs, etc.

More: I noticed that the final requirement mentions visualization; does this imply some development is needed? Are we expected to create software that packages this functionality?

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Interest: This research involves machine learning and data mining, areas I am particularly interested in. I am eager to learn and practice in these fields.

Skill: My skills include data mining and visualizing real-time public transit data, analyzing bus reliability across different times using spatio-temporal datasets, and exploring the relationship between reliability and regional exploitation indices. I also focus on identifying spatial and temporal clusters.

Knowledge: I am familiar with GTFS and real-time (RT) data structures, data mining, visualization techniques, available frameworks for assessing bus reliability, spatio-temporal weighted regression, and cluster analysis.

More: I need help understanding the meaning of "Use analytical tools to understand bus services (e.g., statistical models, visualization techniques, animations, dashboard)."

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(a) why the topic is interesting

Because this project not only highlights the potential for data-driven decision-making in city planning but also emphasises the importance of open data access. Linking mobility data from various sensor systems in Glasgow is particularly interesting as it addresses a significant challenge in urban analytics.

(b) required skills and knowledge of this topic

·       working and learning together throughout the project

·       Time management

·       Active participation

·       Statistical Modeling

·       programming languages such as GIS, R and Python for data cleaning, analysis, and visualization and machine learning methods to extract insights and answer research questions

(c) what you want to know more about it.

I would like to learn about the specific methodologies for linking different types of mobility data and the challenges that may arise in this process. Understanding how to effectively clean and standardise data from sensor system will be crucial for analysis. And also how the research can inform urban planning and policy decisions

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(a) why the topic is interesting

becuase it is relevant in the context of global efforts to combat climate change and achieve Net Zero emissions. The project addresses the practical challenges of retrofitting existing homes in Glasgow, where diverse property types and socio-economic factors come into play.

(b) required skills and knowledge of this topic

·       working and learning together throughout the project

·       Time management

·       Active participation

·       Statistical Modeling

·       programming languages such as GIS, R and Python for data cleaning, analysis

Understanding of renewable heating technologies, specifically heat pumps, and the factors that influence their installation and efficiency

(c) what you want to know more about it.

I would like to learn about the specific methodologies for constructing the dataset and the challenges in integrating data from various sources. i want to have understanding how to effectively analyse socio-demographic factors and their relationship to heat pump adoption would also be interesting. And also understanding in the challenges of retrofitting and how to address them through targeted interventions.

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(a) why the topic is interesting

Because as global temperatures rise, the implications for public health, comfort, and energy consumption become critical. Understanding the patterns of indoor overheating can inform strategies to enhance home resilience, protect vulnerable populations, and reduce energy usage. The project addresses both broader societal goals of sustainable living and effective urban planning.

(b) required skills and knowledge of this topic

·       working and learning together throughout the project

·       Time management

·       Active participation

·       Statistical Modeling

·       programming languages such as GIS, R and Python for data cleaning, analysis, and visualization

·      Understanding of Environmental Science on how indoor climate, thermal comfort, and how socio-economic factors and building characteristics contribute to overheating.

(c) what you want to know more about it.

I would like to know more about the methods for analysing temporal patterns in indoor temperature data, including different types of homes and times of day.

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(a) why the topic is interesting

because rise of platforms like Airbnb has transformed the rental market, creating challenges such as reduced housing availability and increased rents, which directly affect local communities and this research can contribute to developing sustainable housing strategies and ensuring equitable community development.

(b) required skills and knowledge of this topic

·       working and learning together throughout the project

·       Time management

·       Active participation

·       Statistical Modeling

·       programming languages such as GIS, R and Python for data cleaning, analysis, and visualization

·      Data Management and Analysis specially in geocoding and aggregating STL properties from local registries.

(c) what you want to know more about it.

I would like to learn more about the specific methodologies for geocoding and analysing compliance data, specially how to ensure the reliability. I want to Understand the implications of the SIMD in this context and how it can be effectively integrated into the analysis is also of interest.

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(a) why the topic is interesting

Because it addresses a significant gap in urban planning and management. Accurate visitor estimates are crucial for understanding how parks are utilised, which can directly impact resource allocation, maintenance, and the development of park facilities.

(b) required skills and knowledge of this topic

·       working and learning together throughout the project

·       Time management

·       Active participation

·       Statistical Modeling

·       programming languages such as GIS, R and Python for data cleaning, analysis, and visualization

·      Familiarity with the role of parks in urban settings

(c) what you want to know more about it.

I want to understand the methodologies for combining different data sources effectively and ensuring that the models account for potential biases. And also i want to understand how to best regulate the models using available visitation data from New York State parks will be crucial for enhancing the accuracy of estimates

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a) why the topic is interesting

because parks are essential for enhancing the quality of life in cities, and understanding how they are used can inform better management and policy decisions. The COVID-19 pandemic has highlighted the importance of outdoor spaces, making this research is relevant and timely.

(b) required skills and knowledge of this topic

·       working and learning together throughout the project

·       Time management

·       Active participation

·       Data Science and Machine Learning

·       programming languages such as Python and R

·       urban planning principles, public health impacts of parks, and the role of recreational spaces in community well-being.

(c) what you want to know more about it

I would like to explore more about the machine learning techniques best suited for image classification in this context. I am interested in the ethical implications of using social media data for research, including privacy concerns and the representativeness of the data collected. I want to understand how to effectively visualize the spatial distribution of activities and their relationships with points of interest (POIs) would also enhance the project's impact.

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(a) why the topic is interesting

because public transport has a direct impact on daily life and reliablity is a growing concerns in the UK. This could be the reason why most people still drive around Glasgow city centre. This Project stands to address critical societal issues such as accessibility, equity, and sustainability, by providing data-driven insights that can inform policy and operational decisions.

(b) required skills and knowledge of this topic

·       working and learning together throughout the project

·       Time management

·       Active participation

·       Data Analysis and Visualization e.g., R, Python, GIS

·       Understanding of Transport Systems

·       Statistical Modelling and research methodology.

(c) what you want to know more about it

I would like to understand the methods to measure public transport reliability and the challenges assocaited with understanding the data and its interpretation. I am interested in learning where a data-driven approaches could led to significant improvements in public transport systems.