Effects of changing scales on landscape patterns and spatial modeling under urbanization

Spatial scale is an eternal topic in landscape pattern related analysis. This paper examined the spatial scale effect of landscape pattern changes and their relationships with urbanization indicators in Qingdao using a series of sampling blocks. The results indicated that, with the increasing block scale, the mean patch density and aggregation within a block decreased, whereas the diversity increased. Furthermore, the expanding scale amplified the mean change ratio of landscape metrics and eliminated local drastic changes and regional variation trends along an urban-to-rural gradient, which would be obvious at a finer block scale. Meanwhile, the adjusted R2 of GWR (Geographically Weighted Regression) models increased with an increasing block size, especially when the block scale changed from 1 km to 5 km. Odd-numbered block scales performed better than even-numbered block scales

Driving Factors of Land Change in China’s Loess Plateau: Quantification Using Geographically Weighted Regression and Management Implications

Land change is a key topic in research on global environmental change, and the restoration of degraded land is the core component of the global Land Degradation Neutrality target under the UN 2030 Agenda for Sustainable Development. In this study, remote-sensing-derived land-use data were used to characterize the land-change processes in China’s Loess Plateau, which is experiencing large-scale ecological restoration. Geographically Weighted Regression was applied to capture the spatiotemporal variations in land change and driving-force relationships. First, we explored land-use change in the Loess Plateau for the period 1990–2015. Grassland, cropland and forestland were dominant land cover in the region, with a total percentage area of 88%. The region experienced dramatic land-use transitions during the study period: degraded grassland and wetland, expansion of cropland and built-up land and weak restoration of forestland during 1990–2000; and increases in grassland, built-up land, forestland and wetland, concurrent with shrinking cropland during 2000–2015. A Geographically Weighted Regression (GWR) analysis revealed altitude to be the common dominant factor associated with the four major land-use types (forestland, grassland, cropland and built-up land). Altitude and slope were found to be positively associated with forestland, while being negatively associated with cropland in the high, steep central region. For both forestland and grassland, temperature and precipitation behaved in a similar manner, with a positive hotspot in the northwest. Altitude, slope and distance to road were all negatively associated with built-up land across the region. The GWR captured the spatial non-stationarity on different socioeconomic driving forces. Spatial heterogeneity and temporal variation of the impact of socioeconomic drivers indicate that the ecological restoration projects positively affected the region’s greening trend with hotspots in the center and west, and also improved farmer well-being. Notably, urban population showed undesired effects, expressed in accelerating grassland degradation in central and western regions for 1990–2000, hindering forestland and grassland restoration in the south during 2000–2015, and highlighting the long-term sustainability of the vegetation restoration progress. Such local results have the potential to provide a methodological contribution (e.g., nesting local-level approaches, i.e., GWR, within land system research) and spatially explicit evidence for context-related and proactive land management (e.g., balancing urbanization and ecological restoration processes and advancing agricultural development and rural welfare improvement)

Examining Scale-Dependent, nonstationary Environmental Effects on American Lobster (Homarus americanus) Spatial Distribution and Habitat Suitability in a Changing Gulf of Maine

The Gulf of Maine (GOM) is a highly complex environment and previous studies have suggested needs to account for spatial nonstationarity in species distribution models for the American lobster (Homarus americanus). Spatial nonstationarity can be defined as the presence of variation in relationships between independent and dependent variables across space (Windle et al., 2012). To explore impacts of spatial nonstationarity on species distribution, models with the following three assumptions were compared: (1) stationary relationships between species distributions and environmental variables; (2) nonstationary density-environment relationships between eastern and western GOM, and (3) nonstationary density-environment relationships across eastern, central, and western GOM. These comparisons were made amongst generalized additive models (GAMs) to evaluate estimations in lobster spatial distribution, and habitat suitability index (HSI) models to evaluate estimations in lobster habitat suitability. The spatial scales used in these models were largely determined by the GOM coastal currents. Lobster data were sourced from the Maine-New Hampshire Inshore Bottom Trawl Survey from years 2000-2019. We considered spatial and environmental variables including latitude and longitude, bottom temperature, bottom salinity, distance from shore, and sediment grain size in this study. The lobster data utilized in this study were divided into eight groups based on season (fall and spring), sex (female and male), and size (juveniles and adults). Estimates of spatial density and habitat suitability distributions were made for the hindcasting years of 2000, 2006, 2012, 2017, and for the forecasting time period 2028- iii 2055 under the Representative Concentration Pathway (RPC) 8.5 “business as usual” climate warming scenario. We found that the model with the finest scale performed best in both model types tested. This suggests that accounting for spatial nonstationarity in the GOM leads to improved spatial distribution and habitat suitability estimates. Forecasted species distribution estimates revealed that stationary models tended to comparatively overestimate (IQR≅ -36 to 0%) most season sex size group lobster abundances in western GOM, underestimate in the western portion of central GOM, and overestimate in the eastern portion of central GOM (IQR≅ -66 to 29%), with slightly less consistent and patchy trends amongst groups in eastern GOM (IQR≅ -15 to 62% for model 1:2 comparisons and IQR≅ -31 to 28% for model 1:3 comparisons). While in forecasted HSI model estimates, stationary models tended to comparatively overestimate the suitability of habitat for juvenile lobsters (IQR≅ -28 to 1%). For adult lobsters, stationary models estimated higher suitability in both coastal waters in western GOM (IQR≅ -7 to 14%) and farther offshore waters in eastern GOM (IQR≅ -2 to 13% for model 1:2 comparison and IQR≅ -6 to 12% for model 1:3 comparison) than nonstationary models applied at finer scales. Stationary adult HSI models also estimated lower suitability in coastal eastern GOM waters and some offshore western GOM waters as well. The estimated results from stationary and nonstationary GAMs and HSI models were statistically different (

A geographically weighted regression approach to understanding urbanization impacts on urban warming and cooling: a case study of Las Vegas

A surface urban heat island (SUHI) effect is one of the most significant consequences of urbanization. Great progress has been made in evaluating the SUHI with cross-sectional studies performed in a number of cities across the globe. Few studies; however, have focused on the spatiotemporal changes in an area over a long period of time. Using multi-temporal remote sensing data sets, this study examined the spatiotemporal changes of the SUHI intensity in Las Vegas, Nevada, over a 15-year period from 2001 to 2016. We applied the geographically weighted regression (GWR) and advanced statistical approaches to investigating the SUHI variation in relation to several important biophysical indicators in the region. The results show that (1) Las Vegas had experienced a significant increase in the SUHI over the 15 years, (2) Vegetation and large and small water bodies in the city can help mitigate the SUHI effect and the cooling effect of vegetation had increased continuously from 2001 to 2016, (3) An urban heat sink (UHS) was identified in developed areas with low to moderate intensity, and (4) Increased surface temperatures were mainly driven by the urbanization-induced land conversions occurred over the 15 years. Findings from this study will inspire thoughts on practical guidelines for SUHI mitigation in a fast-growing desert city

Insights into heat islands at the regional scale using a data-driven approach

Urban heat island (UHI) phenomenon is crucial in the context of climate change. However, while substantial attention has been given to studying UHIs within cities, our understanding at the regional level still needs to be improved. This study delves into the intricate dynamics of the regional heat island (RHI) by examining its relationship with land use/land cover (LULC), vegetation, and elevation. The objective is to enhance our knowledge of RHI to inform effective mitigation strategies. The research employs a data-driven approach, leveraging satellite data and spatial modeling, examining surface and canopy-layer regional heat islands, and considering daytime and nighttime variations. To assess the impact of LULC, the study evaluates three main categories: anthropized (urbanized), agricultural, and wooded/semi-natural environments. Furthermore, it delves into the influence of vegetation on RHI and incorporates elevation data to understand its role in RHI intensity. The findings reveal meaningful variations in heat islands across different LULCs, providing essential insights. Although urbanized areas exhibit the highest RHI intensity, agricultural regions contribute notably to RHI due to land use changes and reduced vegetation cover. This emphasizes the significant impact of human activities. In contrast, wooded and semi-natural environments demonstrate potential for mitigating RHI, owing to their dense vegetation and shading effects. Elevation, while generally associated with reduced heat island, shows variations based on local conditions. Ultimately, this research underscores the complexity of the RHI phenomenon and the importance of considering factors such as different temperatures and their daily variation, landscape heterogeneity, and elevation. Additionally, the study emphasizes the significance of sustainable spatial planning and land management. Targeted efforts to increase vegetation in high daytime land surface temperature areas can reduce heat storage and mitigate RHI. Similarly, planning for agroforestry and green infrastructure in agricultural areas can significantly increase resilience to climate

A comprehensive spatial-temporal analysis of driving factors of human-caused wildfires in Spain using Geographically Weighted Logistic Regression

Over the last decades, authorities responsible on forest fire have encouraged research on fire triggering factors, recognizing this as a critical point to achieve a greater understanding of fire occurrence patterns and improve preventive measures. The key objectives of this study are to investigate and analyze spatial-temporal changes in the contribution of wildfire drivers in Spain, and provide deeper insights into the influence of fire features: cause, season and size. We explored several subsets of fire occurrence combining cause (negligence/accident and arson), season (summer-spring and winter-fall) and size (100 Ha). The analysis is carried out fitting Geographically Weighted Logistic Regression models in two separate time periods (1988–1992, soon after Spain joined the European Union; and 2006–2010, after several decades of forest management). Our results suggest that human factors are losing performance with climate factors taking over, which may be ultimately related to the success in recent prevention policies. In addition, we found strong differences in the performance of occurrence models across subsets, thus models based on long-term historical fire records might led to misleading conclusions. Overall, fire management should move towards differential prevention measurements and recommendations due to the observed variability in drivers’ behavior over time and space, paying special attention to winter fires.This work has been financed by the Ministerio de Economía y Competitividad; Marcos Rodrigues is a postdoctoral ‘Juan de la Cierva Formación’ research fellow (FJCI-2016-31090); Adrian Jiménez-Ruano is a granted FPU-PhD student (Ref. 13/06618)

지리 가중 회귀모형 및 섀플리 가법 설명모형에 의한 지역침수 영향요인 분석

학위논문(석사) -- 서울대학교대학원 : 환경대학원 환경조경학과, 2022. 8. 송영근.The landscape is considered as a key component of the ecosystem intervention. Human activities have significantly changed the surface characteristics, such as affected the circulation and flow of natural materials and energy, or weakened the rainwater collection, storage function and runoff drainage capacity of the watershed. These led to waterlogging disasters and increased the risk to the living environment. Therefore, landscape planners and decision-makers need to constantly improve and optimize the landscape pattern to maintain the ecosystem's dynamic balance and reduce waterlogging at the same time. Development of remote sensing technology makes it possible to study large-scale watershed units, meanwhile the experiments on such large-scale sites can be verified by theory. Existing research on verification of theories ignored important interactions within the landscape pattern because the traditional linear regression model (a subfield of supervised learning) such as Geographically Weighted Regression (GWR) could not analyze the relationship between independent variables while analyzing the relationship between independent variables and dependent variables. In recent years, development of interpretable machine learning models in the field of machine learning is making up for this shortcoming. Among them, Shapley Additive Explanations (SHAP) is a representative method which provides an interpretable machine learning model based on game theory. It can not only analyze the relationship between independent variables and dependent variables, but also take into account correlations between multiple independent variables, and produce importance ranking according to the contribution degree. Through our extensive and thorough verification and comparative analysis of the two methods, we first find that in the analysis results of GWR, the Shannon Diversity Index (SHDI, one representative landscape metric) is seriously underestimated, while in the results of SHAP, SHDI shows a great impact on waterlogging in any scale of watershed units. At the same time, according to the prediction result of Prediction Mean Squared Error (MSE), although the error value of GWR is small, SHAP is still far more accurate than GWR. Secondly, the water cycle process has characteristics of producing multi-scale geographical watersheds. In order to taking into account the dynamic balance of hydrology, conducting comparative analysis of multi-level watershed-scale units is necessary. Our results show that the use of finer-scale watersheds as the research scale is not necessarily suitable for waterlogging research. In this study, we find that analysis on waterlogging in the Seoul Capital Area (SCA) based on Large-scale watershed units (LSWU) is the most appropriate and accurate. Finally, it is naturally assumed that a threshold for landscape pattern characteristics exists. When the impact on waterlogging reaches this critical point, its role in promoting or alleviating waterlogging will change. Through estimating threshold values of landscape pattern characteristics, the purpose of waterlogging disaster mitigation can be achieved accurately and at a low cost. In summary, this study explores the new analysis method of interactions between landscape patterns and waterlogging, and provid a reference for methods and results of waterlogging control based on landscape ecology.경관은 생태계 개입의 핵심 요소로 꼽힌다. 인류의 활동은 지표면의 특징을 크게 변화시키고 있으며, 자연 물질과 에너지의 순환과 흐름에 영향을 주어 유역에 빗물을 모으는 기능과 경류배수의 능력을 약화시켜 침수 재해의 발생을 초래하고 생활환경의 위험을 증가시킨다. 따라서 경관계획가와 정책결정자는 생태계의 동적 균형을 유지하기 위해 경관구조의 최적화를 끊임없이 개선하여 침수를 완화하는 목적을 달성할 필요가 있다. 원격 탐사 기술의 발달로 대규모 유역 단위 연구가 가능해졌으며, 이러한 대규모 현장에서의 실험은 이론으로 검증될 수 있 다. 이론 검증에 대한 과거의 연구는 지리 가중 회귀 모델(GWR)와 같은 전통적인 선형 회귀 모델(지도 학습)은 독립변수과 종속변수간의 관계를 분석하면서 독립 변수 간의 관계를 분석할 수 없기 때문에 경관 패턴 내의 상호 작용을 무시했다. 최근 머신러닝 분야에서 해석 가능한 머신러닝 모델의 발전이 이러한 단점을 보완하고 있다. 이 중 섀플리 가법 설명모형(SHAP)은 게임 이론에 기반한 해석 가능한 기계 학습 모델의 대표이다. 독립변수와 종속변수의 관계를 분석할 수 있을 뿐 아니라 여러 독립변수의 상관관계를 고려해 기여도에 따른 중요도 순위를 얻을 수 있다. 두 가지 방법의 검증 및 비교 분석을 통해 GWR의 분석 결과에서 섀넌 다양성 지수(SHDI)가 심각하게 과소평가된 반면, SHAP 결과에서 SHDI는 모든 규모의 유역 단위에서 침수에 큰 영향을 미친다는 것을 알 수 있다. 또한 예측 평균 제곱 오차(MSE)의 예측 결과에 따르면 GWR의 오차 값은 작지만 SHAP가 GWR보다 훨씬 정확하다. 둘째, 물 순환 과정은 다단계 지리적 유역을 생성하는 특성을 가지고 있 다. 수문학의 동적 균형을 실현하기 위해서는 다단계 유역 규모 단위의 비교 분석이 필요하며, 그 결과는 더 미세한 유역을 연구 규모로서 사용하는 것이 반드시 수문 연구에 적합하지 않음을 보여준다. 본 연구에서는 대규모 유역단위(LSWU)를 기반으로 한 수도권(SCA)의 침수 연구가 가장 적절하고 정확하다. 마지막으로 경관패턴 특징은 임계치가 존재한다. 침수에 대한 영향이 임계점에 도달했을 때, 침수를 촉진하거나 완화하는 작용이 변화한다. 경관패턴 특징의 임계치를 통해 정확하고 저비용으로 침수 재해를 완화하는 목적을 달성할 수 있다. 본 연구는 경관패턴와 침수간의 상호작용 분석방법에 대하여 새로운 탐구를 진행하여 경관생태학에 기초한 침수 완화방법과 결과를 참고로 제공한다.Chapter 1. Introduction 01 Section 1.1 Urbanization and Human Intelligence 01 Section 1.2 Landscape and Landscape Ecology 02 Section 1.3 Land Use Land Cover and Landscape Pattern Metrics 03 Section 1.4 Natural Water Cycle and Urban Waterlogging 05 Section 1.5 Comparison with Previous Studies 06 Section 1.6 Workflow and Study Area 10 Chapter 2. Materials and Methods 14 Section 2.1 Land Use Land Cover and Landscape Pattern Metrics 14 Section 2.2 Waterlogging Degree of Watershed Units 26 Section 2.3 Geographically Weighted Regression (GWR) 31 Section 2.4 Shapley Additive Explanations (SHAP) 34 Section 2.5 Prediction Mean Squared Error (MSE) 35 Section 2.6 Piecewise Linear Model 36 Chapter 3. Results 37 Section 3.1 Geographically Weighted Regression (GWR) 38 Section 3.2 Shapley Additive Explanations (SHAP) 52 Section 3.3 Prediction Mean Squared Error (MSE) 69 Section 3.4 Piecewise Linear Model 69 Chapter 4. Discussion 76 Section 4.1 Selection of Data and Tools 76 Section 4.2 Supervised Learning and Interpretive Machine Learning 77 Section 4.3 Landscape Threshold and Hydrological Disaster 84 Section 4.4 Rational Use of Limited Land Resources 84 Section 4.5 Limitation and Future Direction 85 Chapter 5. Conclusion 86 Appendix 89 References 90 Abstract in Korean 9

Chronic kidney disease mortality in Costa Rica ; geographical distribution, spatial analysis and non-traditional risk factors

Central America in general and Costa Rica in particular have been facing with increasing number of Chronic Kidney Disease (CKD). Experts have recently (2013) recommended spatial analysis of the relevant data for better understanding of the situation. This study was performed to evaluate geographical distribution of CKD mortality in Costa Rica through spatial analysis of CKD mortality data. The study also looked at associations between CKD mortality and environmental factors. Moreover, this thesis evaluated physicians’ knowledge about non-traditional factors affecting CKD. CKD mortality data (1980 - 2012) were statistically and spatially analysed. Over the study period, CKD mortality showed an upward trend and geographically progressed to the neighbouring areas. Northern parts of the country were identified as the hot spot. Significant associations between CKD mortality and temperature, permanent crops and precipitation were observed (p< 0.05). There were inconsistencies in the effect of temperature and precipitation in different parts of the country. The study also showed an inadequate knowledge of physicians on the possible environmental risk factors for CKD. The findings of this study provided objective evidence on the progressive nature of the CKD problem in Costa Rica. This study also provided further evidence in support of the newly emerging non-traditional risk factors for CKD (agricultural occupation, heat stress etc.). Further investigations are recommended.The two kidneys are vital organs in the body with the main function of filtering out waste products from the blood stream. Chronic kidney disease (CKD) happens when the function of the kidney is not as before which means the kidney is damaged. CKD is now recognised as a global public health issue, but there are areas in the world in which CKD is a more prominent public health issue. One of those areas is Central America. So far, investigations have pointed out several factors as the possible underlying causes of the current CKD increase in Central America, including environmental and occupational factors. In order to better understand the current CKD increase in Central America, we looked at the publicly available data related to CKD deaths in Costa Rica and visualised the findings on Costa Rica’s map. The visualisation was carried out through a modern and sophisticated system called “GIS” or “Geographic Information System”. The maps identified northern part of the country as the hot spot which requires further attention by authorities when allocating resources for public health issues. The maps also showed more CKD related deaths in the geographic areas with more likelihood of exposure to heat and with more farming activities. These findings provide more evidence in support of the likely association between CKD and environmental and occupational factors. Further investigations are recommended

Modelling risk factors in urban residential fires in Helsinki

Asuinrakennuksissa syttyvät tulipalot aiheuttavat merkittäviä henkilö- ja omaisuusvahinkoja erityisesti kaupungeissa. Palojen esiintyvyydellä on todettu olevan voimakas yhteys alueiden ja alueiden asukkaiden piirteisiin, kuten sosioekonomiseen asemaan sekä kotitalouksien ja rakennusten ominaisuuksiin. Vaikuttavat tekijät ovat kuitenkin monimutkaisia ja usein toisiinsa kytkeytyneitä, mikä on vaikeuttanut tarkkojen ennusteiden tekemistä. Riskimallinnus ja paikkatietoanalyysit tarjoavat entistä tehokkaampia ja käytännöllisiä keinoja ilmiön tutkimiseen, erityisesti onnettomuuksien ennaltaehkäisyn ja varautumisen näkökulmasta. Tähän mennessä asuinrakennuspalojen alueelliseen esiintyvyyteen vaikuttavien riskitekijöiden tuntemus Helsingissä on ollut rajallista, mihin tällä tutkielmalla on pyritty tuomaan uutta empiiristä tietoa. Tässä tutkielmassa analysoitiin Helsingissä syttyneitä asuinrakennuspaloja vuosina 2014–2018 250 x 250 metrin ruututasolla. Tulipalojen alueellista riippuvuutta tutkittiin havainnoimalla tilastollisesti merkittäviä palojen keskittymiä. Lisäksi luotiin riskimalli, jolla pyrittiin tunnistamaan tulipalojen alueelliseen esiintyvyyteen vaikuttavia naapurustojen rakenteellisia, sosioekonomisia ja väestöllisiä piirteitä. Menetelminä käytettiin lineaarista regressiota ja spatiaalisen heterogeenisyyden huomioivaa Geographically Weighted Regression (GWR) -menetelmää. Tulokset osoittivat, että asuinrakennuspalot ovat alueellisesti klusteroituneita Helsingissä. Merkittävä suuri keskittymä löytyi kantakaupungin alueelta ja pienempiä keskittymiä Itä-Helsingistä. Tulosten perusteella naapuruston rakenteellisilla piirteillä, sosioekonomisella asemalla ja kotitalouksien ominaisuuksilla on vaikutusta asuinrakennuspalojen esiintyvyyden todennäköisyyteen sekä paloriskiä lisäävinä että vähentävinä tekijöinä. Naapurustotasolla tilastollisesti merkittäviä paloriskiä lisääviä selittäviä muuttujia olivat väestöntiheys, alhainen koulutustaso, työttömyys, asumisväljyys sekä omistusasuminen. Negatiivisesti paloriskiin vaikuttavia tekijöitä olivat asuinrakennusten tiheys, alueen rakennuskannan ikä, korkea koulutustaso sekä myös omistusasuminen. Yleisesti tutkimusalueella tämä kahdeksan muuttujaa selittivät noin puolet asuinrakennuspalojen vaihtelusta. Mallien välisessä vertailussa GWR:n selitysaste oli lineaarista regressiota parempi, ja se myös pystyi tunnistamaan merkittäviä paikallisia eroja selittävien muuttujien vaikutuksissa paloriskiin. Asuinrakennuspalojen riskiin vaikuttavien tekijöiden kokonaisvaltainen ymmärtäminen aluetasolla on tärkeää pelastustoimelle erityisesti valmiuden mitoittamisen ja resurssien tehokkaamman kohdentamisen kannalta. Jatkossa tulisikin kehittää tarkempia malleja, jotta saavutettaisiin entistä kattavampi kokonaiskuva paloriskistä ja siihen vaikuttavista tekijöistä. Erityisesti huomiota tulee kiinnittää tarkemman ja monipuolisemman aineiston ja menetelmien hyödyntämiseen, sekä myös tulipalojen ajallisen ulottuvuuden ja palojen seurauksien sisällyttämiseen mallinnuksessa.Fires in residential buildings can lead to significant personal injury and property damage, especially in cities. Fire incidence has been found to have a strong connection with the characteristics of neighbourhoods and their inhabitants, such as with socioeconomic status and the features of households and buildings. However, the influencing factors are complex and often interconnected, which has made it difficult to make accurate predictions. Risk modelling and spatial data analysis provide effective and practical means of studying the phenomenon, especially from the point of view of accident prevention and preparedness. To date, knowledge of the spatial risk factors affecting residential fire incidence is yet limited in Helsinki. Thus, this study has sought to bring new empirical evidence on the matter. This study analysed residential fires in Helsinki from 2014 to 2018 at a 250 x 250 m grid level. The spatial dependence of fires was investigated by observing statistically significant clusters of fires. In this study, a risk model was created that sought to identify the underlying structural, socioeconomic, and household characteristics of neighbourhoods that affect the likelihood of residential fire incidence. The methods used were linear regression and the Geographically Weighted Regression (GWR), which takes spatial heterogeneity into account. The results showed that residential fires are spatially clustered in Helsinki. A significant large concentration of fires was found in the inner-city area and smaller concentrations in eastern Helsinki. The results indicate that the structural features of the neighbourhoods, socioeconomic status, and household circumstances have an impact on the likelihood of residential fire incidence by both increasing and decreasing the risk of fire. At the neighbourhood level, statistically significant explanatory variables that increased fire risk were population density, low education, unemployment, occupancy rate of dwellings, and home ownership. A negative relationship with fire risk was found with residential building density, age of the buildings, high education, as well as home ownership. Overall, in the study area, these eight variables explained about half of the variance of residential fire incidence. In a comparison between the models, the explanatory power of the GWR was better than linear regression, and it was also able to identify significant local variations in the effects of explanatory variables on fire risk. A comprehensive understanding of the factors influencing residential fire risk at local levels is important for rescue services, especially in terms of planning response readiness and efficient allocation of resources. In the future, more precise models should be developed in order to achieve a more comprehensive understanding of fire risk and the factors affecting it. Particular attention should be paid to the use of more precise and diverse data and methods in modelling, as well as to the temporal dimension and the consequences of fires

中国と日本における都市発展及びその環境への影響の総合評価に関する研究

This study mainly focused on the spatial effect on city development. Spatial analysis was conducted to explore the characteristics and correlates of city development, and its impact on environment for cities in China and Japan. The issue of city development was investigated from multiple perspectives. The history of urban development process in China and Japan was summarized, and the correlates with urban development were compared. Meanwhile, the urban heat island of cities in China and Japan were compared北九州市立大