Urban functional areas are a mirror reflecting the complex interactions between human activities and changes in surface thermal environment. In an era of accelerating urbanization, studying the impact of these areas on the thermal environment has become critically important, especially in light of the increasing environmental changes that the world is experiencing. This article presents an innovative model based on multi-source data to identify urban functional areas in Lanzhou city, exploring the relationship between the effects of human activities and rising temperatures in these areas. We will review how advanced analytical methods, such as Random Forest algorithm and Geographic Regression Models, are used to investigate the factors affecting the urban thermal environment, providing valuable insights for city managers and policymakers to improve the urban environment and promote sustainable development.
Classification of Urban Functional Areas
Urban functional areas are one of the essential elements for understanding the nature of the complex interactions between human activities and changes in the surface thermal environment. The rapid growth of urban areas creates functional areas as effective planning tools that reflect land use and population activity patterns. This study aims to identify how the diversity of human activities affects urban temperature and how it can contribute to understanding thermal changes in these areas. This understanding is based on an analytical model relying on key variables such as “Population Heat – Land Use Scale,” which emphasizes the relationship between population size and land use in determining the impact of each functional area.
The classification of functional areas serves as a tool to analyze how temperatures can vary in different areas, reflecting the diversity in economic and social activities. For instance, residential areas may experience lower temperatures compared to industrial areas due to the absence of intensive activity and the presence of green spaces. Additionally, the study utilizes multi-source data to provide a comprehensive picture of how these human activities affect the thermal environment, facilitating the understanding of the nature of thermal variances and addressing them in urban development plans.
Impact of Urban Activities on the Thermal Environment
The impact of human activities on the thermal environment is one of the fields that have garnered the attention of academics in recent years. Land Surface Temperatures (LST) are a vital indicator for understanding these impacts. Multiple studies indicate that there are notable changes in surface temperatures associated with the distribution of various population activities such as commerce, industry, and logistics services. For example, studies indicate that industrial areas often record higher temperatures due to the density of industrial activities and the resulting heat emissions.
Furthermore, other factors such as vegetation cover and building density effectively influence temperatures. The effect of green spaces can be seen in alleviating heat pressure, providing shade, and helping to cool surrounding areas. Therefore, it is crucial to study these parameters and understand how they can be used to manage and improve urban thermal environments. The analysis offers ways to study the relationship between human activities and environmental factors through techniques such as geographic regression and modeling to uncover complex links that may be absent from traditional analyses.
Utilization of Multi-Source Data in Analysis
Contemporary analyses of urban functional area studies rely on the use of multi-source data. Diverse data, such as remote sensing data, transportation flow data, and social media data, are valuable tools in understanding urban dynamics. With increased access to these technologies, studies integrating economic, social, and environmental elements have increased to achieve a comprehensive understanding.
These data are particularly useful in identifying population patterns and their movements, aiding in more accurately classifying functional areas and analyzing their impact on the thermal environment. For instance, mobile signal data and geographic data were used to classify areas in cities like Shanghai and Chengdu into functional categories reflecting the social and economic activity in those areas. By integrating diverse data, new and significant insights into dynamic processes in urban environments can be gained, facilitating more informed decisions regarding urban planning and resource management.
Models
Current Studies and Future Trends
The study reviews modern trends in using analytical models to understand the complex impacts of human activities on the thermal environment. Many studies rely on models such as random forests and spatial autocorrelation to extract patterns and trends from large data sets. The use of such models indicates significant progress in researchers’ ability to interpret the interrelated effects between urban activities and temperature.
It is also essential to recognize that previous studies often lacked thorough analysis of the implications of development activities in specific areas and their relationship with the thermal environment. This requires expanding the scope of research and analysis to include multiple and interrelated aspects. The results obtained illustrate the importance of accurate and comprehensive data in these studies, as they can lead to clearer conclusions regarding how to improve the quality of the thermal environment in urban areas. The future holds vast potential for developing new tools and models that use data in a more advanced way to enhance understanding and address urbanization and thermal environment issues.
Impact of Human Activities on the Urban Environment
Human activities play a pivotal role in shaping the urban environment and their impact on the local climate. By categorizing these activities into secondary categories such as public health, education, culture, and food services, a clearer picture can be obtained of how they affect the city’s thermal environment. For example, cultural and social activities are considered significant factors influencing temperature variations in urban areas, through increasing social interaction and the use of public spaces, which helps mitigate the effects of urban heat island (UHI) phenomena. These heat islands are exacerbated by human activity, as the increase in buildings and paved surfaces contributes to the heat retained by surfaces. Thus, considering these activities in urban planning can contribute to improving the urban environment overall.
Analysis of Factors Influencing the Urban Thermal Environment
To analyze the factors affecting the urban thermal environment, a set of advanced statistical and spatial methods has been employed. For instance, the “GeoDetector” method and “Geographically Weighted Regression” (GWR) show significant effectiveness in examining the interaction and spatial distribution of various factors. This approach is particularly helpful in studying cities concerned with sustainability issues and achieving a balance between economic development and the local environment. Studies show that academic research is not limited to understanding the impact of individual factors but also seeks to delve into the complex interactions between them, providing accurate data that helps decision-makers implement more effective development plans.
Case Study of Lanzhou City
Lanzhou City serves as a vivid example of the challenges faced by industrial cities under the influence of human activities. The city is located in a river valley, which hinders air movement and leads to the accumulation of air pollutants, thereby exacerbating the heat island phenomenon. Studies indicate that the temperature difference between urban areas of the city and its non-urban surroundings can reach up to 20 degrees Celsius, suggesting that industrial activities in the city have a profound impact on the local climate. Therefore, this city provides an ideal model for understanding how climatic factors affect industrial cities and the necessity for environmental improvements.
Application of Advanced Technical Methods in Environmental Research
Research on urban thermal changes relies on using advanced techniques to obtain accurate data, such as “Landsat 8” data that aid in analyzing land surface temperatures. Advanced algorithms are utilized to process high-resolution data to transform the thermal extraction process into a precise and comprehensive one. Spatial techniques like bivariate spatial correlation analysis provide greater insights regarding how heat is distributed across different functional areas in the city. All these methodologies enhance scientific understanding of the urban environment and support systematic decision-making.
Conclusion
Future Improvements in Urban Planning
Effective urban planning requires a comprehensive consideration of the city environments. Human activities must be integrated into the development of planning strategies, which in turn can help mitigate the negative impacts on the thermal environment. Additionally, investing in new technologies for environmental monitoring and data analysis is essential, contributing to informed decision-making that supports the sustainability of cities. Cities like Lanzhou can serve as a model in taking effective steps towards developing a more sustainable urban environment by combining academic research with strategic planning for sustainable development.
Assessment of Surface Temperature in Urban Areas
The topic of assessing surface temperature in urban areas is discussed through quantitative and graphical methods, which have been employed to understand the differences in surface heat across various urban regions. The research collects data on land surface temperature (LST) using techniques such as regression analysis and spatial match metrics. Highlighting the importance of measuring graphical relationships between average temperatures and standard deviation of the LST model. Typical temperature values were determined, and urban areas were divided into study units characterized by specific features. These vital stages are based on multiple visual data sources, such as GF-2 images, which help compile roadway information and provide higher accuracy in urban area studies.
Analysis of Urban Functional Areas
The analysis of urban functional areas is a crucial component of the research mission, where the city is divided into units based on the road network and land use zones. Geographic data such as POI (Points of Interest) data were utilized to gather information on economic activities and demographic dynamics. To develop a functional weight model, POI data were integrated with mobile data to study population interactions, allowing for a comprehensive insight into the functional activities within the city. Two main categories were considered: population density and land use scope. These functions are directly related to the provision of all public services required by the city and determining how those services are distributed across various urban areas.
Data and Techniques Used to Analyze the Thermal Environment
Studying the thermal environment in urban areas requires a variety of techniques including analysis of variance (ANOVA) and the use of random forest algorithms to analyze the differences across diverse thermal environments. ANOVA is used to determine if there are statistically significant differences between different areas. This method is closely related to assessing the impacts of variables that may lead to differences in surface temperatures. While the random forest algorithm is employed to measure the accuracy of estimates and analyses, allowing for a deeper understanding of the effects of different functional areas on surface heat. These methods are not only concerned with understanding heat patterns, but also with providing recommendations on how to improve city design to achieve lower temperatures in urban areas in the future.
Factors of Change in Thermal Surface Environment
Factors of change in the thermal surface environment are examined through their multidimensional effects. The GeoDetector method was employed to identify the strength of the interactive effects of various factors, aiding in the interpretation of temperature shifts in urban areas. This approach allows for establishing a relationship between independent variables (such as building technology, land use, and population density) and dependent variables (temperatures). Geographically supported regression models were also introduced to accurately characterize the effects across different research units, providing a flexible understanding of changes in thermal environments and identifying the most influential factors. This helps guide urban plans and improve city design to alleviate challenges associated with rising temperatures.
Characteristics
Spatial Distribution of Surface Thermal Environment
The spatial characteristics of thermal distribution have been studied as an important part of the research to understand thermal distributions in the city of Lanzhou. The study is conducted at specific times of the year, such as August, when the highest temperatures are recorded. Analyzing the distribution of surface temperature helps identify areas with high and low heat, demonstrating how central areas may have different interactions compared to marginal areas. The collected data indicate that high concentrations of commercial and industrial activities can signal the persistence of elevated temperatures in those regions. This can be observed by comparing temperatures in appropriate areas with meteorological data from scattered stations across the city, providing a scientific basis for inferring environmental impacts and future plans.
Analysis of Temperature Effects and Urban Regulations on Surface Thermal Environment
Current research indicates a strong relationship between surface temperatures and urban activities, where an advanced algorithm was utilized to reverse temperature readings and analyze climate station data. The results of the analysis show that the high consistency level of this algorithm lends significant credibility to the outcomes of surface temperature reversals. In 2024, correlation analysis conducted by (Huang R. et al.) showed a notable positive correlation, with a correlation coefficient of 0.7125 and a p-value less than 0.05, indicating strong statistical significance.
One key finding of this study is the tables and important data reflecting the distribution of temperatures among different city areas. This is linked to the analysis of the spatial characteristics of urban functional areas using weighted models that consider population density and consumption patterns. The results indicated that industrial areas occupy the largest share of space, while the financial services area had the least. These discrepancies in functional distribution had a direct impact on the surface thermal distribution in the city.
Distribution of Urban Functional Areas and Their Impact on Thermal Environment
Recent research values the importance of analyzing a specific functional area in its impact on the surface thermal environment. After mapping the functional areas, it is notable that surface thermal energy was elevated in areas concentrated with social activities and life services. A chaos matrix was used to assess the accuracy of the functional definitions, revealing an overall accuracy exceeding 84%. This metric is considered very good for urban data analysis.
When urban areas are categorized into classes, and a range of life and industrial activities included, it can be observed that population density and economic activity profoundly affect the distribution of surface temperature. For instance, the industrial sector has a greater impact on elevating thermal rates due to the highways used and the accumulation of commercial and industrial activities, leading to thermal instability in those areas.
Statement on Thermal Differences in Urban Areas and Ways to Address Them
Research was conducted through a series of statistical analyses to illustrate the surface thermal differences among various urban areas. ANOVA testing demonstrated thermal differences across different urban regions, confirming that these differences are statistically significant. The relevant data includes the distribution of surface temperatures and their impact on the surrounding environment, showing that temperatures are significantly higher in urban areas with high activities.
Studies also examined how these temperature discrepancies affect urban quality of life, as excessive heat contributes to worsening pollution and the emergence of health-related diseases. Green infrastructure and sustainable urban planning are considered strategic solutions but require a deep understanding of thermal distributions and population clusters. Additionally, the link between human activities and the thermal environment reflects the need for improved urban planning to achieve a balance in heat distribution.
Assessment
Factors Affecting Surface Thermal Environment
The factors affecting the surface thermal environment include natural mediators such as topography, and human interventions such as building density. The study of the participating factors classified a set of measurable indicators and used modern techniques such as random forest algorithms to improve the accuracy of predictions. It focused on how surface heat responds to changes in land use and building density.
During the study, it was determined that the most influential factors include land cover type and industrial activity density, as areas with vegetation cover and lower building heights were less impactful. The analysis showed that the random forest algorithm was effective in estimating how these factors influence thermal conditions, highlighting the necessity of using modern analytical tools in future studies.
Spatial Distributions of Surface Temperature and Their Relation to Human Activities
This section delves deeply into the characteristics of spatial distributions of surface temperature and their relationship to the type of human activities in cities. Measurements of spatial autocorrelation were conducted, and thermal clusters were identified according to different functional types. The results showed that there are high thermal clusters in industrial and service areas, while green spaces experienced lower temperature levels. This indicates a correlation between land use and thermal distribution.
Identifying such patterns gives us an in-depth insight into how urban heat increases and reflects an urgent need to recognize the impact of urban form and planning. Understanding the dynamics between various activities and surface temperatures can lead to better policies to enhance comfortable living spaces and reduce negative phenomena associated with rising temperatures, such as urban heat islands.
The Impact of Built-Up Areas on Surface Temperatures in Lanzhou
Lanzhou, characterized by its industrial nature and population density, exhibits a clear impact of built-up areas on surface temperatures, contributing to the urban heat island (UHI) phenomenon. This impact is associated with the high percentage of built-up spaces in the city center, where this factor is considered one of the main reasons affecting surface temperature. These effects include not only the area of built-up regions but also the nature of the materials used in construction, which reflect or absorb heat in different ways. For example, materials used in building construction such as concrete and asphalt contribute to heat retention, increasing the temperature of surrounding areas.
Additionally, vegetation plays an important role in reducing surface temperatures through effects derived from evaporation, as the presence of green spaces helps regulate the urban thermal environment. In Lanzhou, there is a well-developed green infrastructure, highlighting the importance of vegetation in maintaining moderate temperatures compared to surrounding areas. Therefore, the presence of green spaces in the urban center is one of the key factors contributing to improving the urban climate.
The Impact of Industrial Activity on the Thermal Environment
Lanzhou is predominantly an industrial city, where industrial activities significantly contribute to energy consumption and heat emissions. Scientific research reports show that industrial activity is concentrated in the Zigou area, which is considered the main industrial base in the city. Here, industrial processes enhance the urban heat island phenomenon, as factories release additional heat into the surrounding air. This effect manifests in areas suffering from industrial congestion, which is closely related to the thermal emissions produced by machines and vehicles used in the production process.
Examples of specific industrial activities, such as the manufacturing of construction materials, can be discussed, which are often warm due to the thermal processes applied. This significantly contributes to raising the surrounding temperatures, exacerbating the heat island effect. Alternatively, it is essential for the city to manage these industrial activities sustainably, by utilizing more energy-efficient technologies and systems to improve thermal efficiency, which may help reduce the negative thermal impacts on the urban environment.
Interaction
Different Factors and Their Impact on Surface Temperatures
Studies show the use of an interaction detection model that reveals the complex relationships between various explanatory factors of the heat island phenomenon. The GeoDetector model was used to uncover these interactions, and the results indicate that the interaction among different factors is not linear, but exhibits enhanced reinforcement, meaning that the joint effect of two factors can be much greater than the effect of each individually. This is inferred from empirical values exceeding 0.7, indicating the importance of the interaction of these factors in shaping the thermal environment.
For instance, the results show that areas with dense vegetation often correlate with high levels of industrial activity, contributing to increased surface temperatures in those areas. This phenomenon is also reinforced by high population density in some neighborhoods, combined with the availability of green spaces. Therefore, understanding these interactions represents an important step towards developing effective strategies for improving urban environmental management and mitigating thermal impacts.
Spatial Analysis of Factors Affecting Surface Temperatures
Geographic regression models were used to understand the spatial variation in the impact of different factors on surface temperatures in urban work areas. The results of the studies indicate that key factors such as land use characteristics, building density, and vegetation cover significantly affect the average temperatures in those areas. Data shows that high building density often increases surface temperatures, while vegetation cover plays an opposing role.
Areas with especially high temperatures were identified in industrial and commercial neighborhoods, where the results of the spatial analysis reflect how architectural designs and the distribution of urban elements contribute to formulating the thermal environment within the city. There is an increasing need for urban planning processes that consider the spatial changes that may occur due to human activities, in addition to the necessity of enhancing green and water areas to lower temperatures.
Recommendations to Mitigate Heat Island Effects in Urban Areas
Addressing the heat island phenomenon in Lanzhou requires implementing a series of strategies sensitive to the city environment. Urban planning is a central part, where future policies must include plans to build green spaces capable of creating a better thermal balance. Necessary studies can be conducted to identify areas needing green additions and to adapt daily life areas to improve quality of life.
Planning for rehabilitation of natural environments, such as planting trees and developing water areas, is considered an effective solution. Recommendations also include improving building designs regarding the materials used and number of floors, so that the design contributes to enhancing air circulation and reducing heat retention. In addition to strengthening land protection legislation for unbuilt areas, an integrated ecosystem can be developed in urban planning to enhance several environmental features.
Impact of Local Climate Zones on Urban Climate
Local climate zones (LCZ) are an effective tool for understanding the impact of surface properties and coverage on local climate, as demonstrated by research conducted by Liu and others in 2017. These studies, such as the one by Zeng and others in 2024, highlight the impact of nine LCZ areas in Beijing on heat island occurrences in urban areas. Researchers used LCZ data with a resolution of 120 meters, along with temperature and humidity data collected from automated weather stations, and found that open areas were the primary contributor to the urban heat island (UHI) and urban dry island (UDI) phenomena. This indicates the importance of understanding the structure of cities and the effect of various land uses on urban climate.
Thermal Variations Between Urban Functional Areas
Studies have shown that the intensity of the urban heat island phenomenon is much higher in areas with high population density compared to open areas. For example, Chen and others in 2023 analyzed the impact of the UHI phenomenon in six LCZ areas in Guangzhou, using temperature data collected over 405 days. While the focus was primarily on the spatial distribution and physical characteristics of urban areas, it is also necessary to recognize the importance of human activities and their impacts on urban temperatures. The integration of local climate systems and urban functional areas provides a more comprehensive understanding of how both physical characteristics and human activities affect the local climate.
Challenges
Measurement and Challenges in Data Collection
Despite the significant benefits of LCZ systems, there are considerable challenges related to data collection and analysis. For example, images captured by Landsat 8 provide observations of surface temperatures only during the day, which hinders monitoring the surface thermal environment in urban functional areas at night. Moreover, existing constraints in the characteristics of Points of Interest (POI) data and classification labels hinder the improvement of urban zoning. This leads to a gap in understanding how low-density populated areas and their surroundings affect the urban thermal environment.
Conclusions on Spatial Diversity of Surface Temperature
Research results show that land surface temperatures (LST) significantly vary between different areas within the city, being lower in areas close to the Yellow River, while higher in surrounding areas. This diversity is increasingly distributed among functional areas, where industrial and agricultural organized areas overlap more with thermal areas. Understanding this diversity is essential for developing effective urban strategies to mitigate heat impacts in cities. Furthermore, the results highlight the importance of diversity in human land uses and how each use can contribute to raising or lowering urban temperatures.
Future Tools and Methods for Thermal Data Analysis
In the future, multidimensional data is expected to be used to expand our understanding of the temporal characteristics of thermal variation in the urban environment based on zoning. This relies on the use of advanced models such as random factor models to understand the factors contributing to the thermal environment. Future studies should include simultaneous analysis of data during the day and night to obtain a comprehensive picture of how urban activity affects temperatures. Additionally, research should focus on improving the ability to recognize Points of Interest data and enhancing understanding based on the functional characteristics of civilizations.
The Importance of Urban Adaptation in the Face of Global Warming
Global warming is one of the foremost challenges facing major cities around the world, resulting from increased greenhouse gas emissions due to human activities. In recent years, urban adaptation concepts have been adopted as an effective strategy to mitigate the impacts of this phenomenon. Urban adaptation encompasses a range of policies and measures aimed at enhancing cities’ ability to adapt to climate change and reduce negative impacts.
Urban adaptation can help lower city temperatures by promoting green spaces, such as creating public parks and providing tree shade. For instance, studies have indicated that increasing the proportion of green spaces can reduce the urban heat island effect, improving the quality of life for city residents. Sustainable urban planning, which involves distributing activities in a way that positively impacts the environment, is another pathway for adapting to climate change.
Furthermore, the construction of green buildings and sustainable technologies in buildings plays a significant role in urban adaptation. These buildings reduce energy consumption and improve resource efficiency, making them capable of facing the challenges posed by a changing climate. Smart systems for energy control and air conditioning within buildings are examples of how this goal can be achieved.
However, urban adaptation is not merely a technical issue; it must also include a social perspective. Adaptation strategies should incorporate the views and lessons of local communities and their practices to ensure the effectiveness of the implemented applications. Engaging local populations in adaptation plans and giving them a role in the decision-making process is crucial, as they represent the primary stakeholders affected by these policies.
Identification
Functional Areas in Ancient Cities and Their Impact on Sustainable Development
In many countries, ancient cities stand out with their architectural complexities and rich history. It is essential to identify the functional areas within these cities to understand how they can benefit from sustainable development. Points of Interest (POI) can be used in analyzing functional areas, which helps in formulating new urban planning strategies that align with the preservation of cultural heritage.
Analyzing these areas using geographic point data can be an effective way to identify commercial, residential, and service locations in ancient cities. For example, studies on ancient cities can help pinpoint areas that need rehabilitation or those that are attractive to tourists, striving to maintain the cultural identity of the city while meeting economic and social needs.
This approach also requires consideration of the impacts of changes on the environment and economy. For instance, increasing urban regeneration necessitates careful planning to achieve a balance between development and the preservation of green spaces. Maintaining this balance is key to sustainable development that can benefit local communities.
Another example is the Fusou initiative, which used POI analysis to identify various architectural functions in the city. Through this initiative, the different needs of the areas were studied and assessed, greatly contributing to the development of urban planning strategies that account for sustainable growth and cultural heritage preservation.
Analyzing Environmental Impacts of Urban Functional Areas through Big Data
Utilizing big data is a powerful tool for understanding the impacts of functional areas in cities. This data can reveal usage patterns, traffic movement patterns, and interactions between various environmental factors. For example, linguistic analytical studies and correlation analysis can be used to understand how urban activities affect surface temperatures.
Temperature levels are crucial metrics for measuring the impact of functional areas. Thermal data can reflect the level of thermal emissions generated by human activities. Big data can help determine the relationship between city structure, economic growth, and quality of life. Here lies the importance of having high-resolution data to efficiently assess areas.
A program such as the “Heat Reduction Model” is an example of the effective use of big data, measuring surface temperature in different areas of the city and contributing to an understanding of how to mitigate the effects of urban heat islands. Such programs should be an essential part of environmental monitoring strategies as they help inform urban planners about environmental changes.
Finding big data-based solutions is a vital step towards achieving a balance between urban growth and sustainable environments. By leveraging technology, such as advanced data analytics and artificial intelligence, cities can adapt to climate change more effectively and achieve sustainable development goals.
The Impact of Rapid Urbanization on Urban Thermal Environment
Rapid urbanization reflects the drastic changes that have taken place in human settlements and production methods over the past decades. With increasing human activities such as residential living, industrial production, and urban transportation, built areas are continuously expanding. These changes lead to alterations in the physical characteristics of the urban surface and increased heat emissions resulting from human activities, contributing to the Urban Heat Island (UHI) phenomenon. Studies indicate that this phenomenon poses a serious threat to urban quality of life and public health in urban environments.
Research on the impacts of urban thermal environments and their regulators has garnered increased attention in recent years, with surface temperature being utilized as a quantitative indicator of the state of this environment. This research reflects various impacts such as changes in land use, landscape patterns, city extent, and urban functions. Several metrics are used in these studies, including network metrics, block units, and local climatic zones, each with its own advantages and disadvantages. Research shows that urban functional areas possess unique characteristics that reflect the complex interactions between human activities and surface thermal environment.
It is evident
of urban functional areas on thermal environment
Urban functional areas are the fundamental units for city planning and management, reflecting the spatial distribution of economic and social activities. Urban functional areas are typically classified based on land use patterns, such as residential, commercial, and industrial areas. Research has shown that these areas exhibit significantly different thermal characteristics, with industrial and commercial zones displaying much higher temperatures compared to residential zones.
These temperature differences result from several factors such as building density, vegetation cover, and usage patterns. For example, areas with higher building densities tend to retain heat during the night, leading to increased temperatures in those zones compared to more open areas with greater vegetation cover. Therefore, planning and regulatory processes in urban areas require a thorough understanding of temperature variations between different regions.
Addressing urban heat island issues is essential to mitigate climate change effects and enhance quality of life. Proposed strategies should include tree planting, increasing green spaces, and using reflective construction materials to reduce heat absorption. Additionally, considerations must be made on how factors like public transportation and the distribution of social services play a role in the sustainability of urban functional areas and their contribution to positively modifying the thermal environment.
The role of data in understanding urban thermal patterns
Modern technology has revolutionized the quality of data available for studying thermal patterns in cities. The use of aerial imagery, satellite-based data, and big data analytics has provided a new window into studying urban functional areas and social and productive interactions in greater depth. Various technologies such as traffic data from taxi services, social media data, and GPS applications have been utilized to understand how thermal patterns are influenced by individuals’ daily activities.
For instance, studies like those utilizing taxi movement data to analyze daily mobility in Chengdu are examples of how data is leveraged to understand diverse functional patterns. By categorizing these activities, thermal characteristics associated with each type of activity were identified, allowing researchers to grasp the direct impact of human activities on the thermal environment.
Employing new data sources to delineate functional areas is crucial for enhancing the understanding of interactions between human activities and the environment. These interactions are not static but continually evolve with changes in economic and social behaviors; thus, modern urban planning should rely on precise, data-supported analyses to ensure sustainability and efficiency.
Transport data and mobile signals in urban area analysis
Mobile signal data plays a prominent role in studying population distribution and active pathways in urban areas. This data provides continuous dynamic records that make it an effective tool for analyzing high-frequency human activities. However, it should be noted that the data may face limitations in terms of location accuracy and the number of participants, which could lead to the omission of certain important urban functional characteristics. To overcome this point, Points of Interest (POI) data are considered a good complementary factor that can compensate for the lack of geographical elements in mobile signal data. By integrating these two types of data, a more comprehensive depiction of urban functions can be achieved, including thermal fluctuations resulting from human activities in the city. Studies also demonstrate how secondary categories such as healthcare, educational, and cultural services are closely linked to residents’ lives, providing clearer insights into the impacts of various human activities on the urban thermal environment.
The impact
Urban Heat and Driving Factors
Urban heat phenomena (UHI) extend to being a problem experienced by many industrial cities, including Lanzhou. These phenomena reflect how the continuous accumulation of pollutants, resulting from industrial activity, leads to rising urban temperatures. Researchers are interested in using a variety of analytical methods, including univariate quantitative analysis that often focuses on the relationship between different factors and their impact. However, recent research demonstrates the importance of utilizing more advanced models such as the “GeoDetector” model, which can analyze interactions between different factors and their spatial distribution. These tools allow researchers to understand the relationship between urban functions and surface thermal environment. Furthermore, techniques such as bivariate spatial correlation analysis can contribute to formulating more precise urban planning strategies, helping to design healthy and sustainable urban environments.
Environmental Impacts in Lanzhou City
Lanzhou City is located in a mountainous river region and faces many environmental challenges associated with industrial activity, including the urban heat island effect. Recent studies have shown an increase in temperature between the city and undeveloped lands ranging from 10.00°C to 20.00°C between 2001 and 2021. This rise is attributed to the concentration of industrial activities over the decades and increasing urbanization that accompanies rising emissions of pollutants. This is particularly vital for cities like Lanzhou, where air movement is limited due to topographical constraints, preventing the dispersion of pollutants into the atmosphere. Therefore, it is essential to have precise urban planning strategies that consider maintaining a healthy environment and mitigating the negative impacts of human activity on the environment.
The Methodological Framework of the Study
This study is based on a comprehensive methodological framework that integrates multiple-source data, including point of interest data, mobile signal data, and satellite imagery. A thoughtful model was designed to analyze the relationship between urban functions and the surface thermal environment. This model employs advanced analytical techniques such as Random Forest algorithms to analyze the contribution of each type of urban functional area to the thermal environment. Methods such as bivariate spatial analysis were also introduced to understand the spatial variations of the thermal environment. This approach enhances the city’s ability to improve its environmental policies and urban planning, providing planners with substantial tools for achieving sustainable development.
Data Sources and Processing Operations
The study relied on a variety of data including mobile signal data, point of interest data, and satellite images that were collected and processed meticulously. For example, Landsat 8 images were utilized to sort surface temperatures in urban areas through detailed analysis of the data components, such as the formatting and structure of the information derived from these sources. POI data was classified into 21 types, leading to the accurate identification of 10 types of urban functional areas, reflecting differences in the thermal environment. The use of multiple-source techniques such as DEM and night-time light data provides a comprehensive view of the urban environment and its impact on climatic conditions, enhancing the effectiveness of urban planning strategies.
Interpretation of the Equations Used to Estimate Surface Temperatures
The equations presented by Chen et al. (2022) are essential for understanding how to estimate surface temperatures using advanced mathematical models. The equations refer to a complex balance between several influencing factors such as surface ground temperature (Tsensor) and atmospheric temperature (Ta), along with parameters like surface emissivity (ε) and atmospheric transmissivity (τ). The first equation shows how to calculate temperatures based on these factors, indicating that any change in one of these factors will affect the estimated surface temperature.
The second and third equations revolve around a structural interpretation of the emissivity and penetrability elements, which are not only individual traits but have a compounded effect that reflects the general state of the environment. For example, if the land in a particular area has a high level of emissivity, this may lead to elevated temperatures, especially in urban areas that consist of construction materials that increase greenhouse effects. This demonstrates how changes in environmental properties can affect temperatures, a sensitive point for sustainable urban area management.
Spatial Classification of Urban Temperatures
The process of classifying temperature in urban areas is not merely a computational matter; it requires a deep understanding of spatial variation between different regions. This complex aggregation demands accurate data, such as surface temperature measurements, which are processed using standard methods like the standard deviation method. The proposed equation for classification reflects a set of regions so that they are classified into areas of low or high temperatures based on specific criteria.
For example, in the city of Lanzhou, this equation may show temperature variations due to local influences such as the presence of rivers or vegetation cover. Through this type of classification, local authorities can make informed decisions regarding land use regulation, improve transportation systems, and plan green spaces, all for the purpose of enhancing the quality of life.
Analysis of Urban Functions and Demographic Distribution
It is essential to understand the role of geographical information in analyzing urban dynamics, especially when it comes to a functional area like the city of Lanzhou. Road networks such as OSM are used to analyze routes and the relationships between various services, where the kinetic data of individuals leads to a better understanding of how urban resources are utilized. Using models such as the weighted two-factor prevailing functions model, which holds implications about the relationship between population density and land use, statistics like the number of Points of Interest (POI) provide a comprehensive view of the city’s functions.
Connecting geographical data with kinetic information facilitates the detection of clinical patterns of the relationship between populations and urban resources, providing valuable information for authorities regarding improving urban planning and better investment of resources. For instance, the study may find that areas with high population density are losing green spaces or essential services, necessitating immediate intervention for equitable resource distribution among inhabitants.
Strategies for Analyzing Surface Thermal Environment
Strategies such as Analysis of Variance (ANOVA) and Random Forest models are used to understand how thermal environments differ among various functional areas within a city. Interpreting ANOVA results allows for the identification of significant temperature differences between areas, aiding in intelligent data aggregation. For instance, if results show that a certain area consistently maintains elevated temperatures, that area may be targeted by urban planning policies addressing greenhouse issues.
Random forest models support this analysis by mining data to extract patterns and hidden knowledge that may not be apparent through traditional analysis. Thus, these patterns can determine how green spaces may influence the thermal composition of matched areas or transport, enhancing the effectiveness of the strategies applied.
Driving Factors Distinguishing the Surface Thermal Environment
To identify the driving factors that contribute to disparities in surface thermal environments, tools like GeoDetector and weighted geographical models are employed to analyze the relationship between various variables. For example, GeoDetector can be used to reveal the potential impacts of factors such as population density, materials used in buildings, and proximity to green spaces. These analyses help in understanding how environmental and social factors can interlace and affect the quality of the urban environment.
Furthermore,
Weighted Geographic Weighted Regression (GWR) models are used to represent the non-stationary relationships between variables, allowing for the discovery of complex interactions that may affect both surface environments and urban planning decisions. By exploring local variations in the effects of these factors, planners can make decisions designed to reduce disparities in living conditions, improve land-use efficiency, and enhance environmental quality in urban areas.
Climate Data Analysis and Surface Temperature Estimation
Climate station data, along with a Landsat 8 image obtained at a specified time, were used to estimate land surface temperatures (LST) in Lanzhou city. A regression method was utilized to verify the accuracy of the results, where average temperatures were collected from climate stations at 11:00 AM and 12:00 PM local time, and then compared to the average LST calculated from an area consisting of three pixels. The results show that the average LST was approximately 5.7 degrees Celsius higher than the observed average air temperature, reflecting the natural variance between land surface temperature measurements and air temperatures.
An analysis was conducted using Pearson correlation coefficient to measure the relationship between measured data and calculated data, where the results demonstrated a strong positive correlation with a correlation coefficient of 0.7125 and a p-value of less than 0.05, indicating the reliability of the results derived from the algorithm used in temperature estimates. These results signify the high capability of the methodology followed in providing accurate models for land surface temperature measurements.
Spatial Characteristics of Urban Functions
A binary factor model was employed to identify the various functional characteristics of urban areas in Lanzhou city. The results indicated that industrial areas occupy the largest proportion of space, while financial service areas represent the smallest percentage. These results reflect the spatial distribution of functional areas, with industrial areas concentrated in large zones within the “Xigu” sector, while the population density of residential areas is higher in the east and south. Science, education, and culture-sports are combined in the “Chengguan” and “Anning” areas, reflecting the importance of educational resources there.
To assess the accuracy of identifying urban functional areas, a confusion matrix was used to estimate classification accuracy. A total of 120 samples were requested to analyze the accuracy of these identifications, and the final result reflected an overall accuracy of 84.3% and a Kappa coefficient of 0.826. These results reflect the effectiveness of the methodology used in determining urban functions and assist in guiding urban planning based on local needs.
Surface Thermal Distinction in Urban Functional Areas
Studies were conducted to shed light on the relationship between different functional areas and surface thermal environments. The results of ANOVA analysis demonstrated significant differences in LST between different functional areas, supporting the hypothesis of the significant impact of various urban activities on the thermal environment. The Random Forest algorithm was utilized to estimate the contribution of each area to the thermal environment, where it was proven that the life service area had the greatest impact due to high population density and energy consumption.
The impact of functional areas on the surface thermal environment was arranged in ascending order, with the life service area ranking first, followed by the industrial area, while green areas had the least impact. These results demonstrate the need for sustainable strategies in urban planning to mitigate the excessive thermal impacts resulting from human activities.
Spatial Analysis of Thermal Environmental Factors
Research focused on the factors affecting the surface thermal distribution in urban functional areas. Data analysis models were developed to determine the role of factors such as urban activities, land use, and building properties in influencing the thermal environment. By using multiple analyses, high-precision data were prepared to distinguish the independent effects of each factor.
Results showed
The results indicate that in addition to urban growth, urban planning and the distribution of greenery in cities play a crucial role in affecting temperature levels. The analysis focused on how the effects of different fields vary, using multiple metrics to validate scientific hypotheses about their impact. This comprehensive understanding enables policymakers and planners to develop effective solutions for adapting to climate changes and ensuring urban sustainability.
Testing the Significance of Urban Environmental Effects
Urban environmental effects are a critically important topic in studies related to climate change and the degradation of urban environments. The significance test shows links between land cover types, the density of industrial activity, vegetation density, and their impacts on urban surface temperatures. The results indicate that the NDBBI (Normalized Difference Built-up Index), FVC (Fractional Vegetation Cover), and POI-KDI (Point of Interest – Kernel Density Index) all reflect strong impacts on the thermal environment in urban areas. The presence of q values exceeding 0.4 indicates that the thermal environment of urban neighborhoods is significantly influenced by the aforementioned factors in addition to urban density and the nature of urban areas.
For example, studies show that areas with high density of buildings and bare land have a greater capacity to absorb solar radiation, leading to a significant increase in surface temperatures in downtown Lanzhou. In contrast, areas with high vegetation cover contribute to lowering temperatures through the process of plant transpiration. This highlights the necessity of having strong green infrastructure to regulate the urban climate and improve air quality.
Monitoring Complex Interactions Between Influential Factors
The study of interactions among various factors is an essential element of understanding their impacts on the thermal environment. The study used GeoDetector models to analyze how different factors interactively influence outcomes. The results showed a non-linear enhancement between various influencing factors, where the explanatory power resulting from the interaction of any two factors together was greater compared to the impact of either factor alone. For instance, interactions between NDBBI and FVC with POI-KDI had q values exceeding 0.8, indicating the importance of their joint effects.
The interactions between these factors showed that bare land coverage and building density are often associated with intense commercial activities, thereby enhancing the Urban Heat Island (UHI) phenomenon in multiple areas. Moreover, indicators associated with human activity such as MPSI (Mobile Phone Signal Index) and NLI (Night Light Index) showed a lesser effect when used alone, but their impacts were heightened when interacting with other factors.
This monitoring provides a deeper understanding of how human activities and environmental factors influence city environments, necessitating consideration of strategies to mitigate the negative impacts of these activities by modifying urban design and regional planning.
Spatial Variability Analysis of Influencing Factors
Spatial analysis is a powerful tool for assessing how different factors affect surface temperature in urban areas. GWR models (Geographically Weighted Regression) were used to study the spatial variability of influencing factors, and the results showed significant variability in the impacts among different factors. The intersections of factors such as NDBBI, FVC, and POI-KDI exhibited different responses in various terrains and urban contexts.
For instance, the results indicate that NDBBI had a positive impact on the thermal environment, with regression values ranging from 0.028 to 0.409 in the Chengguan area. Meanwhile, the FVC index showed a negative impact on temperatures, with regression values ranging from -0.099 to -0.006, indicating that areas with high vegetation density contributed to lower surface temperatures.
The value
POI-KDI has also proven to increase the effect of thermal environments in urban areas, as areas with high values showed a positive effect on heat, such as the Xigu neighborhood. This analysis highlights the need to integrate environmental considerations into urban planning, especially concerning building density, distribution of green spaces, and the impact of industrial activities.
Sustainable Challenges and Solutions for Urban Management
As the UHI phenomenon worsens, it becomes essential to explore and propose effective ways to address the challenges arising from rising temperatures in urban environments. Research shows that the areas most exposed to high heat include residential, service, and industrial areas, necessitating adjustments and improvements in urban planning and environmental preservation.
Focus should be placed on implementing improvements in urban design by reducing building density while increasing green spaces and the presence of water bodies. Introducing elements such as greening on roofs and walls is considered an effective solution to enhance the city’s climate. Furthermore, urban planning should consider the role of green spaces as essential elements for mitigating urban heat impacts.
It is also important to reinforce government policies to protect unbuilt land and encourage sustainable buildings. Strategies should be developed that focus on improving urban quality of life by reducing thermal emissions and enhancing natural connections. All these factors will contribute to creating more sustainable and livable urban environments.
Statistical Analysis Strategies in Studying Urban Climate Changes
Geographically weighted regression (GWR) models are among the effective tools for analyzing local relationships in spatial data, as they take into account the impact of geographical changes on the relationships produced in statistical models. For instance, researchers used GWR models to examine the variations of factors affecting urban carbon emissions in China, demonstrating their importance in studying urban climate dynamics. This requires understanding how factors like relative humidity and temperature interact, and how these interactions lead to patterns of urban heat.
It should also be noted that the local climate zone (LCZ) has shown a significant impact on the relationship between the spatial distribution of various environmental elements and their effects. Based on previous studies, it can be said that open LCZ areas were the main contributor to the formation of the urban heat island (UHI) and urban dry island (UDI) phenomena. By utilizing satellite data and images such as Landsat, researchers can depict the complex relationship between urban patterns and heat indicators, providing a clear basis for urban environmental planning and management.
This system’s knowledge is not limited to measuring temperatures but includes analyzing land use types and their relationship to human activities and their effects on the urban environment, thus promoting sustainable urban development. It can be concluded that there is an urgent need to collect multi-temporal data that reflects urban climate variability in different planning areas.
The Interaction of Human Activities and Their Impact on Thermal Environment in Cities
Human activities are a pivotal factor in shaping the urban thermal environment, where land uses and locations of economic activity significantly determine temperature levels in different areas of the city. Studies have shown that population density, the level of industrial development, and life service networks directly affect surface temperature levels. For example, industrial areas may contribute to rising temperatures due to thermal activity generated by production processes.
Through an analysis provided by the random forests model, the impact of each land use type on the thermal environment can be assessed. The results indicate that life service areas have contributed the highest impact on temperature increases, followed by industrial areas and restaurants, while green areas and government service departments had the least impact. This reveals the interconnected effects between urban patterns and urban climate, necessitating more sustainable planning mechanisms.
However,
Regarding the urban park system or urban forest, the availability of green spaces significantly helps reduce temperatures and enhances the quality of urban life. Therefore, integrating natural solutions such as increasing green spaces into smart city layouts should be a top priority to achieve a balance between economic development and climate preservation.
Methods for Identifying Thermal Patterns and Spatial Distribution in Modern Cities
Identifying thermal patterns is one of the most important topics that require continuous research and development. Modern technologies demand the use of multi-source data such as satellite images and big data analysis to identify areas with high temperature levels and environmental considerations in urban planning. For instance, Landsat 8 data has been used to estimate surface temperatures and monitor changes in land use spectrums.
Research highlights the importance of studying the relationship between land use patterns and temperatures. Analytical methods such as neural networks and other analytical algorithms have been employed to understand the more complex relationships between these patterns. Specific areas have been identified as “hot spots,” meaning these areas experience a continuous increase in temperatures due to increased urban density and economic activity.
Moreover, these studies should be accompanied by awareness efforts and the involvement of local communities in discussions related to urban planning, especially in understanding how ordinary activities affect the urban climate. Cities need comprehensive strategies that include community engagement to achieve environmental and social benefits.
The Impact of Using Satellite Data in Improving Urban Environmental Management
Satellite data offers tremendous tools for assessing and analyzing climatic conditions and urban heat dynamics. This data is used to aggregate accurate information about surface temperatures and land cover conditions, aiding the creation of a comprehensive understanding of the environmental impacts associated with urban growth. This data enhances research processes and supports policymakers in enacting effective policies that promote sustainable development.
Remote sensing technologies help provide timely assessments through which effective management strategies can be developed. For example, surface temperature changes can be tracked periodically using various satellite images, facilitating the monitoring of changes in elements of the urban environment. This enables cities to respond quickly to challenges posed by climate change.
Additionally, this data should be utilized in urban planning and in building strategies aimed at reducing the impacts of the locations of economic activities on the ecosystem. The integration of satellite data with local and model data improves the accuracy of decisions made and provides actionable information that supports the long-term planning framework for the city. In conclusion, leveraging these technologies contributes to developing comprehensive visions for a sustainable urban future that meets the current needs of the population and their future challenges.
The Importance of Analyzing Social Data in Urban Planning
Social data is an essential element in understanding urban patterns and modern city planning. This data contributes to providing a clear vision of how residents interact with the urban environment. When accurate information about residents’ daily activities is available, planners can use this information to enhance urban designs and allocate services more effectively. For instance, analyzing social data derived from social media can help identify tourist attraction areas or the distribution of public facilities such as schools and hospitals.
The use of social data can also provide insights into residents’ preferences. By analyzing data extracted from mobile applications, such as visit rates and attendance at public places, planners can understand whether the city meets its residents’ needs or not. In the case of Beijing, for example, studying the social patterns associated with public spaces led to redesigning some facilities to meet the needs of various age groups.
Developing
Data analysis techniques such as machine learning can also contribute to improving our understanding of social data. For example, by using machine learning algorithms, large datasets can be analyzed more quickly, identifying patterns that may be invisible when looking at the data in a traditional manner. This can help achieve efficiency in local authorities’ responses to changing needs.
Impact of Urban Distribution on Surface Temperature
Research indicates that urban form has a significant impact on rising temperatures in urban areas, especially in densely populated regions. The uneven distribution of resources such as green spaces and water contributes to the formation of urban heat islands. In major cities such as Beijing, data shows a noticeable increase in temperatures due to the increase of winding spaces and tree removal.
Architectural patterns and city planning can lead to different thermal effects. For example, recent studies show that cities with high urban density, with lots of concrete and glass, are more susceptible to the heat island effect compared to cities with green spaces. Therefore, integrating sustainable concepts into urban designs, such as green areas and parks, can help reduce temperatures and preserve the environment.
The “cool pavements” model is one of the prominent solutions in this context. This model relies on the use of materials and components in construction to reduce heat absorption. Studies show that using white or reflective surfaces can lead to significantly lower temperatures, subsequently reducing energy consumption.
Impact of Climate Change on Urban Areas
Cities today are suffering from the impacts of climate change, which include rising sea levels, extreme weather phenomena, and drought. These challenges require a robust response from urban planners and decision-makers. For example, the phenomenon of rising temperatures can affect traffic patterns and energy efficiency, necessitating adjustments in urban layouts to address these challenges.
Geographic data analysis techniques are a powerful tool to support climate change adaptation strategies. By using Geographic Information Systems (GIS) and climate data, cities can identify the most vulnerable areas and plan appropriate adaptation strategies.
Initiatives like “climate-resilient cities” are examples of how ecologists and planners use scientific methods to achieve sustainable development. This program aims to improve urban planning by integrating climate resilience into all aspects of urban development.
Urban Heat Analysis Using Machine Learning Data
The current applications of machine learning in analyzing urban heat data represent a modern trend in understanding climate challenges in cities. This analysis relies on using large data collected from multiple sources, including satellites, environmental sensors, and mobile phone information. By using deep learning methods, data can be processed quickly and efficiently to reveal patterns and trends.
For example, machine learning techniques can be used to understand the relationship between land use and surface temperatures. By linking data extracted from simulation models with surface data, underlying factors that contribute to rising temperatures can be identified, aiding in the development of strategies to mitigate this phenomenon.
Machine learning applications, such as weighted geographic regression models, enhance the accuracy of climate models by providing a more complex understanding of the interactions between environmental and thermal factors. This technological advance enhances decision-makers’ ability to take effective steps using real and accurate data.
Source link: https://www.frontiersin.org/journals/environmental-science/articles/10.3389/fenvs.2024.1466542/full
Artificial intelligence has been used ezycontent
“`
Leave a Reply