Analysis temperature variations in Melbourne and set mitigation strategies

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Project Title

Analysis temperature variations in Melbourne and set mitigation strategies

Introduction

Urban usually get much hotter than the countryside. This situation referred to as the urban heat island (UHI) effect. Urban areas like Melbourne face challenges related to temperature variations, which can impact public health, energy consumption, and overall urban livability. This project will investigate how temperatures change across Melbourne to better understand the severity and spread of UHI and to find the methods to lessen its effects.

Topic and Research Question

Topic: Urban Heat impact in Melbourne

Research Question: What are the spatial distributions and intensities of urban heat islands across Melbourne, and discuss what urban factors contribute to these situations, and how can mitigation strategies be implemented to address urban heat island effects?

Project Scope

Geographic Area: Melbourne, Victoria, Australia

Spatial Scale: The study will collect and analyze temperature data from eight locations in Melbourne.

Time Scope: The project will analyze data from a series of sensor readings that collected temperature, light, and humidity every 5 minutes during the trial period from 2014 to 2015 to capture seasonal and long-term temperature trends. This will provide a detailed understanding of the urban heat island effects during the year.

Proposed Data Sources

Point Data: sensors’ location

Polygon Data: Fitzroy Gardens and Library at the Dock in Melbourne

Raster Data: humidity, light, temperature

Cartographic Presentation

A preliminary map will be created to show Melbourne's suburban boundaries, by overlaying data from temperature sensors to visualize Melbourne's temperature changes, highlighting hot spots and areas of concern. Where possible, we include thematic maps illustrating vegetation cover in the area to identify potential mitigation strategies.

Literature Review

The phenomenon of urban heat islands has acquired significant attention due to its influence on human habitat. Research indicates that urban heat islands primarily occur because cities use materials that absorb heat all day and release it at night (Oke, 1982). This effect is compounded by the absence of evapotranspiration from vegetation in urban areas. According to a comprehensive study by Rizwan, Dennis, and Liu (2008), compared to their rural surroundings, UHIs can increase urban temperatures by 1 to 3°C. These temperature discrepancies have various influence including increased energy consumption, elevated emissions of air pollutants and greenhouse gases, and more severe heatwaves, which affect public health (Stone Jr, 2005).

Further research by Zhou and Shepherd (2010) utilized satellite imagery to analyze how urban planning influences UHI effects. Their findings suggest that cities with densely packed buildings and less green space tend to have higher UHI intensities. The strategies for mitigation, such as the development of green roofs, increased urban tree on streets, and the use of lighter-colored construction materials have been shown to reduce UHI effects significantly (Santamouris, 2014).

These findings on the urban heat island effect really highlight why it is crucial to carefully plan cities and actively manage the environment to reduce the negative impacts of the urban heat island effect. The ongoing analysis of UHI effects not only helps in adapting urban areas to be more resilient to heat but also contributes to broader climate change mitigation strategies.

In order to evaluate the urban heat island effect, we need simulations of microclimates. Accurate modeling of microclimate dynamics in complex urban environments requires significant computing power. , so we may introduce a hybrid Python approach to simulate temperature changes in cities. We read a python model proposed by Mansoureh Gholami, which combines various engines in Rhinoceros to explain the interaction between urban surfaces, tree canopies and atmosphere, which may give us some ideas for model building in this project .

Proposed Methodology

The approach will involve using Spatial Data Analysis to find patterns and areas where temperatures are consistently high, including interpolation methods to estimate temperature distributions, hot spot analysis to identify areas with extreme temperatures, etc. And then we will use advanced techniques like geographically weighted regression to explore how different factors contribute to the unusual temperatures, making the related maps and discuss them. In this project, Jupyter Notebook will be the primary tool for analyzing the collected data and performing spatial analysis. We'll leverage Python to efficiently manipulate, visualize, and analyze sensor data.

Hypothesis

It is hypothesized that urban areas with high building density and limited vegetation cover will experience higher temperatures compared to green spaces and parks. Mitigation strategies such as increasing green infrastructure and implementing cool roof initiatives can help reduce temperature extremes in these areas.

Expected Results

The study is expected to produce detailed maps highlighting the intensity and spatial distribution of Melbourne's temperatures, and then explore which areas within them are more severely affected by the urban effect. Where possible, we expect to identify spatial patterns of temperature changes in Melbourne and propose targeted mitigation strategies to alleviate the urban heat island effect. The findings from this project will help develop recommendations for urban design and green infrastructure to reduce thermal effects and adapt to future climate change.