Due to the intensity of urban development around the world, there is an increasing body of studies attempting to investigate urban heat island (UHI) in various spatial and temporal scales. In surface ...heat urban island (SUHI) studies, extended periods of time, broader regions and local government area (LGA) level have become more crucial and will shed light on causes of UHI. Moreover, the spatial pattern and structure of SUHI will be useful for policy-makers to develop mitigation strategies. This study focused on three objectives. Firstly, analyzing land surface temperature (LST), normalized difference built-up (NDBI) and vegetation (NDVI) indices. Secondly, investigating interrelationships among LST, NDVI, and NDBI. Thirdly, identifying LST patterns in the Melbourne metropolitan area. These objectives were achieved through three different methods. The modified automatic mapping method for the first objective, the correlation analysis for the second, and spatial statistical methods for the third. The methodological innovations of this study were considering LGA in interrelationship analysis among LST, NDBI and NDVI, and calculation of NDVI for each acquisition date. The results indicated that the clustering pattern of LST expanded toward the north-west and south-east during the period of the study. Furthermore, the north-west part of the city has the highest positive (0.6) correlation between NDBI and LST, and the south-east part of the city has the lowest negative (−0.8) correlation between NDVI and LST. The most significant increase and decrease in mean LST happened respectively from January 6th to 22nd 2017, and January 14th to 30th January 2014. The temperature degree altered from 19.61 °C to 27.86 °C in inner western suburbs, and from 35.49 °C to 26.88 °C in most LGA's. These findings are critical for planners to localize UHI mitigation action plans, target hot spots in LGA's and allocate resources to respond to the adverse effect of UHI.
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•Time-series satellite imagery and spatial statistical used to determine the LST spatial structure at the local government level.•LST calculated by the modified mapping method and NDVI calculated for each acquisition dates from Landsat satellite images.•Overall, clustering patterns of LST expanded toward the north-west(NW) and south-east(SE) in Melbourne•NW part has the highest positive correlation between NDBI and LST; SE part has lowest negative correlation for NDVI and LST
Hot weather not only impacts upon human physical comfort and health, but also impacts the way that people access and experience active travel options such as walking and cycling. By evaluating the ...street thermal environment of a city alongside an assessment of those communities that are the most vulnerable to the effects of heat, we can prioritise areas in which heat mitigation interventions are most needed. In this paper, we propose a new approach for policy makers to determine where to delegate limited resources for heat mitigation with most effective outcomes for the communities. We use eye-level street panorama images and community profiles to provide a bottom-up, human-centred perspective of the city scale assessment, highlighting the situation of urban tree shade provision throughout the streets in comparison with environmental and social-economic status. The approach leverages multiple sources of spatial data including satellite thermal images, Google street view (GSV) images, land use and demographic census data. A deep learning model was developed to automate the classification of streetscape types and percentages at the street- and eye-view level. The methodology is metrics based and scalable which provides a data driven assessment of heat-related vulnerability. The findings of this study first contribute to sustainable development by developing a method to identify geographical areas or neighbourhoods that require heat mitigation; and enforce policies improving tree shade on routes, as a heat adaptation strategy, which will lead to increasing active travel and produce significant health benefits for residents. The approach can be also used to guide post COVID-19 city planning and design.
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•A human-centred perspective for street thermal comfort assessment with eye-level images•Fine scale neighbourhood heat vulnerability index was developed.•Low thermal comfort street sections in high heat vulnerable neighbourhoods were prioritised.•The framework can be adapted by decision makers to enhance the climate resilience of streets.
Many countries have set a goal for a carbon neutral future, and the adoption of solar energy as an alternative energy source to fossil fuel is one of the major measures planned. Yet not all locations ...are equally suitable for solar energy generation. This is due to uneven solar radiation distribution as well as various environmental factors. A number of studies in the literature have used multicriteria decision analysis (MCDA) to determine the most suitable places to build solar power plants. To the best of our knowledge, no study has addressed the subject of optimal solar plant site identification for the Al-Qassim region, although developing renewable energy in Saudi Arabia has been put on the agenda. This paper developed a spatial MCDA framework catering to the characteristics of the Al-Qassim region. The framework adopts several tools used in Geographic Information Systems (GIS), such as Random Forest (RF) raster classification and model builder. The framework aims to ascertain the ideal sites for solar power plants in the Al-Qassim region in terms of the amount of potential photovoltaic electricity production (PVOUT) that could be produced from solar energy. For that, a combination of GIS and Analytical Hierarchy Process (AHP) techniques were employed to determine five sub-criteria weights (Slope, Global Horizontal Irradiance (GHI), proximity to roads, proximity to residential areas, proximity to powerlines) before performing spatial MCDA. The result showed that ‘the most suitable’ and ‘suitable’ areas for the establishment of solar plants are in the south and southwest of the region, representing about 17.53% of the study area. The ‘unsuitable’ areas account for about 10.17% of the total study area, which is mainly concentrated in the northern part. The rest of the region is further classified into ‘moderate’ and ‘restricted’ areas, which account for 46.42% and 25.88%, respectively. The most suitable area for potential solar energy, yields approximately 1905 Kwh/Kwp in terms of PVOUT. The proposed framework also has the potential to be applied to other regions nationally and internationally. This work contributes a reproducible GIS workflow for a low-cost but accurate adoption of a solar energy plan to achieve sustainable development goals.
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While it is well recognised that increasing vegetation cover reduces the Urban Heat Island (UHI) effect in cities, less is understood about the spatial pattern of vegetation type ...required to maximise cooling benefits. This study examines how different urban vegetation spatial configuration and composition impact on the UHI phenomenon. We investigated this on a set of sites in Metropolitan Melbourne, Australia. Urban vegetation raster data at 20 cm resolution was used to define five height-based vegetation cover types (grass, shrub, small, medium and large trees); and to calculate eight landscape metrics: percentage of landscape (PLAND), mean patch area (AREA_MN), patch density (PD), edge density (ED), mean patch shape index (SHAPE_MN), mean Euclidean nearest-neighbour distance (ENN_MN), Shannon’s diversity index (SHDI) and Landscape shape index (LSI). These vegetation landscape metrics of vegetation types were established as independent variables and statistically analysed with the UHI intensity as the dependent variable using Classification and Regression Tree analysis (CRT). The CRT model was developed based on 6469 records and including depth of 5, 41 nodes and 21 terminal nodes. From the total 43 independent variables, 11 were identified as high impact factors on UHI intensity. Our findings revealed a consistent negative statistical relationship between UHI intensity and PLAND-landscape, PLAND-large tree and PLAND-medium tree across the study sites. The PLAND, ED and AREA_MN were the most prevalent metrics to explain UHI effect, which has also principally demonstrated the joint impact of the three metrics on UHI effect. This study presents a new framework of a fine-scale assessment and modelling for the impact of urban vegetation on UHI; and elaborates a practical approach of using CRT technique to design local-based UHI mitigating strategies taking advantage of different vegetation structure.
Non-renewable-resource consumption and global greenhouse-gas (GHG) emissions are critical issues that pose a significant threat to sustainable development. Solar energy is a promising source to ...generate renewable energy and an appealing alternative electricity source for households. The primary goal of this research is to detect the rooftops that have no solar photovoltaic (PV) system deployed on them but that receive moderate to high solar-energy radiation using the Geographic Information System (GIS) and deep-learning techniques. Although various studies have been conducted on this subject, not many addressed these two issues simultaneously at a residential level. Identifying the installed solar PV systems in a large area can be expensive and time-consuming work if performed manually. Therefore, the deep-learning algorithm is an emerging alternative method to detect objects using aerial images. We employed the Single-Shot-Detector (SSD) model with the backbone of residual neural network 34 (ResNet34) to detect the solar PV systems and used GIS software to compute solar isolation and calculate the electricity production estimate (EPE) of each rooftop. Our results show that the SSD model detected 6010 solar panels on 4150 properties with an accuracy of 78% and observed that there were 176 Statistical Area 1s (SA1s) that had no rooftops with solar PV systems installed. Moreover, the total electricity production from the suitable area was estimated at over 929.8 Giga Watt-hours (GWhs) annually. Finally, the relation between solar-PV-system density and EPE was also identified using the bivariant correlation technique. Detecting the existing solar PV systems is useful in a broad range of applications including electricity-generation prediction, power-plant-production management, uncovering patterns between regions, etc. Examination of the spatial distribution of solar-energy potential in a region and performing an overlay analysis with socio-economic factors can help policymakers to understand the explanation behind the pattern and strategize the incentives accordingly.
Abstract
Assessing vulnerability to natural hazards is at the heart of hazard risk reduction. However, many countries such as Australia lack measuring systems to quantity vulnerability for hazard ...risk evaluation. Drawing on 41 indicators from multiple data sources at the finest spatial unit of the Australian census, we re-forged the Cutter’s classic vulnerability measuring framework by involving the ‘4D’ quantification of built environment (diversity, design, density and distance), and constructed the first nationwide fine-grained measures of vulnerability for urban and rural locales, respectively. Our measures of vulnerability include five themes—(1) socioeconomic status; (2) demographics and disability; (3) minority and languages; (4) housing characteristics; and (5) built environment—that were further used to assess the inequality of vulnerability to three widely affected natural hazards in Australia (wildfires, floods, and earthquakes). We found the inequality of vulnerability in the affected areas of the three hazards in eight capital cities are more significant than that of their rural counterparts. The most vulnerable areas in capital cities were peri-urban locales which must be prioritised for hazard adaptation. Our findings contribute to the risk profiling and sustainable urban–rural development in Australia, and the broad understanding of place-based risk reduction in South Hemisphere.
Identifying and quantifying ecotourism opportunities are critical processes in sustainable tourism planning, which is challenging, since ecotourism is a Complex Adaptive System (CAS). This study ...investigated Ecotourism Opportunities Measurements (EOMs) in the literature and mapped the research trends to provide practical implications for research in this area. A systematic quantitative literature review began with a scientometric analysis in CiteSpace to examine the existing knowledge and the state of the art in EOMs. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) protocol was then applied to refine the initial search results, and snowballing was used to collect additional articles. The refined set was then thematically coded and quantitatively analyzed. Our findings show that existing studies on ecotourism opportunities predominantly focus on the impacts of ecotourism on the environment, stakeholders’ contributions toward ecotourism development, sustainability, and responsible behavior of local communities in ecotourism promotion. In addition, five dimensions have been identified under which ecotourism opportunities can be measured, including nature, environmental education/protection, sustainability, socio-cultural benefits, and tourist satisfaction. Existing scales or indices assess potential destinations qualitatively rather than quantitatively. In contrast, an index-based approach might help to solve the challenges of evaluating ecotourism opportunities as a CAS, as well as to quantitatively assess potential destinations to support decision-making related to ecotourism promotion.
Journey to crime describes the spatial patterns of offenders from their residential area to the crime location. When compared to other research topics regarding urban crime, there is still a lack of ...research on journey to crime, especially in China, as a result of which the behavioural motivation of offenders cannot be comprehensively examined. Four typical types of crimes committed against property (pickpocketing, robbery, theft and burglary) were investigated in the Nanguan District of Changchun from 2010 to 2016. The results showed significant effects of the demographic characteristics of offenders and spatiotemporal factors on the journey to crime. In terms of the place of household registration, offenders from the central urban districts of Changchun tend to commit short‐distance local robbery, whereas those from the suburban counties tend to commit long‐distance non‐local crimes. With increasing population density, the proportion of local plunders increases directly. This study aims to encourage urban managers to rethink the governance of floating populations, and assist police in strengthening social security.
Recent developments in Western Australia's economy including widespread traffic congestion as well as road safety issues are increasingly becoming prominent. Previous studies relied on traditional ...statistical methods to investigate patterns and characteristics of motor vehicle crashes. Although useful, statistical analysis alone is incapable of providing a spatial context and is therefore unable to associate existing crash characteristics with a spatial distribution.
To identify concentrations or "hotspots" of articulated heavy vehicle crashes in WA between the years 2001-2013, by using a spatial analysis approach.
Spatial modelling and spatio-temporal analytical methods such as Emerging Hotspots were used to identify emerging hotspots on specific roads in Western Australia using the Integrated Road Information System (IRIS).
The results suggest that the majority of articulated heavy vehicles crashes occurred in the vicinity or within the Perth metropolitan area. Based on spatial-temporal trend analyses, our findings highlight some regions that are emerging as areas of interest.
This study was one of the first attempts to adopt a spatial analysis approach in studying heavy-vehicle crashes in Western Australia. Applying spatial methodologies to road safety data has the potential of obtaining previously undiscovered insights, which can be extended further, and provide future avenues to research in this field.
This study examines college youth travellers' food choices using a market segmentation-based latent class modelling approach. A D-efficient stated-preference experiment was conducted in four Chinese ...cities, identifying two tourist market segments with different preferences. Key factors influencing food choices include cost, taste, waiting time, service responsiveness and noise levels. Elasticities, first differences and willingness-to-pay values are estimated, providing insights for effectively targeting the youth tourism market. Results suggest that improving service quality and reducing noise levels can significantly enhance food appeal, while higher quality yields marginal returns. These findings can assist tourism and catering agencies in devising efficient strategies to attract youth travellers.
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