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The purpose of this paper was to develop an integrated framework for assessing the flood risk and climate adaptation capacity of an urban area and its critical infrastructures to help address flood risk management issues and identify climate adaptation strategies.

Using the January 2011 flood in the core suburbs of Brisbane City, Queensland, Australia, various spatial analytical tools (i.e. digital elevation modeling and urban morphological characterization with 3D analysis, spatial analysis with fuzzy logic, proximity analysis, line statistics, quadrat analysis, collect events analysis, spatial autocorrelation techniques with global Moran’s I and local Moran’s I, inverse distance weight method, and hot spot analysis) were implemented to transform and standardize hazard, vulnerability, and exposure indicating variables. The issue on the sufficiency of indicating variables was addressed using the topological cluster analysis of a two-dimension self-organizing neural network (SONN) structured with 100 neurons and trained by 200 epochs. Furthermore, the suitability of flood risk modeling was addressed by aggregating the indicating variables with weighted overlay and modified fuzzy gamma overlay operations using the Bayesian joint conditional probability weights. Variable weights were assigned to address the limitations of normative (equal weights) and deductive (expert judgment) approaches. Applying geographic information system (GIS) and appropriate equations, the flood risk and climate adaptation capacity indices of the study area were calculated and corresponding maps were generated.

The analyses showed that on the average, 36 (approximately 813 ha) and 14 per cent (approximately 316 ha) of the study area were exposed to very high flood risk and low adaptation capacity, respectively. In total, 93 per cent of the study area revealed negative adaptation capacity metrics (i.e. minimum of −23 to <0), which implies that the socio-economic resources in the area are not enough to increase climate resilience of the urban community (i.e. Brisbane City) and its critical infrastructures.

While the framework in this study was obtained through a robust approach, the following are the research limitations and recommended for further examination: analyzing and incorporating the impacts of economic growth; population growth; technological advancement; climate and environmental disturbances; and climate change; and applying the framework in assessing the risks to natural environments such as in agricultural areas, forest protection and production areas, biodiversity conservation areas, natural heritage sites, watersheds or river basins, parks and recreation areas, coastal regions, etc.

This study provides a tool for high level analyses and identifies adaptation strategies to enable urban communities and critical infrastructure industries to better prepare and mitigate future flood events. The disaster risk reduction measures and climate adaptation strategies to increase urban community and critical infrastructure resilience were identified in this study. These include mitigation on areas of low flood risk or very high climate adaptation capacity; mitigation to preparedness on areas of moderate flood risk and high climate adaptation capacity; mitigation to response on areas of high flood risk and moderate climate adaptation capacity; and mitigation to recovery on areas of very high flood risk and low climate adaptation capacity. The implications of integrating disaster risk reduction and climate adaptation strategies were further examined.

The newly developed spatially explicit analytical technique, identified in this study as the Flood Risk-Adaptation Capacity Index-Adaptation Strategies (FRACIAS) Linkage/Integrated Model, allows the integration of flood risk and climate adaptation assessments which had been treated separately in the past. By applying the FRACIAS linkage/integrated model in the context of flood risk and climate adaptation capacity assessments, the authors established a framework for enhancing measures and adaptation strategies to increase urban community and critical infrastructure resilience to flood risk and climate-related events.

The purpose of this paper is to explore the sustainable development strategy of green finance under the background of big data.

From the perspective of big data, this paper uses quantitative and qualitative analysis methods to judge the correlation among green finance, environmental supervision and financial supervision. Green finance gives the entropy method to calculate the score of green finance and environmental regulation, and establishes the spatial lag model under the double fixed effects of time and space.

Spatial autocorrelation test shows that economic spatial weight matrix has obvious spatial effect on green innovation. Through the model selection test, the space lag model with fixed time and space is selected. The regression coefficients of green finance, environmental regulation and their interaction are 0.1598, 0.0541 and 0.1763, respectively, which significantly promote green innovation. The regression coefficients of openness, higher education level and per capita GDP are 0.0361, 0.0819 and 0.0686, respectively, which can significantly promote green innovation.

In view of the current situation of large-scale application of big data technology in green innovation of domestic energy-saving and environmental protection enterprises, this paper establishes a fixed time lag evaluation model of green innovation. M-test statistics show that the original hypothesis with no obvious spatial effect is rejected.

There is a lack of understanding in the literature on the spatial relationships between crime and house price. This paper aims to test the impact of spatial effects in the housing market, how these are related to the incidence of crime and whether effects vary by the type of crime.

The analysis initially explores univariate and bivariate spatial patterns in crime and house price data for the Belfast Metropolitan Area using Moran’s I and Local Indicator Spatial Association (LISA) models, and secondly uses spatial autoregression models to estimate the role of crime on house prices. A spatially weighted two-stage least-squares model is specified to analyse the joint impact of crime variables. The analysis is cross sectional, based on a panel of data.

The paper illustrates that the pricing impact of crime is complex and varies by type of crime, property type and location. It is shown that burglary and theft are associated with higher-income neighbourhoods, whereas violence against persons, criminal damage and drugs offences are mainly associated with lower-priced neighbourhoods. Spatial error effects are reduced in models based on specific crime variables.

The originality of this paper is the application of spatial analysis in the study of the impact of crime upon house prices. Criticisms of hedonic price models are based on unexplained error effects; the significance of this paper is the reduction of spatial error effects achievable through the analysis of crime data.

Taking 30 provincial samples from 2001 to 2017 in mainland China as the research objects, this paper aims to evaluate the impact and effects of foreign direct investment (FDI) on the urban–rural income gap and reveals heterogeneity across regions.

Firstly, the Theil index is used to measure the income gap between 30 provinces in mainland China from 2001 to 2017, then the spatial econometric model is used to empirically test the impact of foreign direct investment on China’s urban–rural income gap and its heterogeneity across regions. Finally, a robustness test is performed.

The results show that there is a significant inverted U-shaped relationship between FDI and the urban–rural income gap in China. That is, FDI expands the urban–rural income gap in the short term and helps to converge it in the long term. In the eastern region, FDI has a convergence effect on the urban–rural income gap in the short term, which increases the long term. However, in the central and western regions, the relationship between FDI and urban–rural income gap has a weak inverted U shape.

By assessing the impact of FDI on the urban–rural income gap, this work provides decision-making support for China and other developing countries to improve investment policies and income distribution policies.

Globally, corruption has been identified as a major problem. Even though corruption is widespread, it varies in magnitude, types and consequences. In Nigeria, corruption is endemic, and it is responsible for the many socioeconomic problems in the country. Hence, the study aims to determine the patterns and state level correlations of corruption in Nigeria.

Data for this study were sourced from the National Bureau of Statistics and other official sources and were analyzed with Global Moran’s I, Local Moran’s I and multivariate step-wise regression.

This study’s findings revealed significant clustering of corruption in the country with Rivers States as the only hotspot (I = 0.068; z = 2.524; p < 0.05), while domestic debt and market size were the state level significant predictors.

Only bribery as a form of corruption was examined in this study, more studies are needed on the predictors of other forms of corruption.

This study recommends increased market competition through investment grants, subsidies and tax incentives to facilitate trade interactions among Nigerians, which can lead to exchange of cultural norms that discourage corruption. It is also advocated that domestic debt must be effectively and efficiently channelled towards economic development which in the long run will have a positive impact on the socio-economic well-being of the citizens as well as drive down corrupt practices.

Although the causes of corruption have received considerable attention in the literature, little is known on the geographical distribution and the effect of market size and domestic debt on corruption in Nigeria.

As the contradiction between economic development, resource and environment has become increasingly prominent, low-carbon competitiveness has received worldwide focus. This study aims to examine low-carbon competitiveness in 31 provinces (cities and regions) of China.

An evaluation index system for low-carbon competitiveness in China has been constructed, which is composed of 25 economic, social, environmental and policy indicators. To study the state of low-carbon competitiveness and resistance to China’ development of low-carbon competitiveness, this study uses a combination of the catastrophe progression model, the spatial autocorrelation model and the barrier method.

China’ low-carbon competitiveness gradually decreases from coastal to inland areas: the Tibet and Ningxia Hui autonomous regions are the least competitive regions, while the Shandong and Jiangsu provinces are the most competitive areas. The spatial correlation of the 31 provinces’ low-carbon competitiveness is very low and lacks regional cooperation. This study finds that the proportion of a region’ wetland area, the proportion of tertiary industries represented in its GDP and afforestation areas are the main factors in the development of low-carbon competitiveness. China should become the leader of carbon competitiveness by playing the leading role in the Eastern Region, optimizing the industrial structure, improving government supervision and strengthening environmental protection.

The paper provides a quantitative reference for evaluating China’ low-carbon competitiveness, which is beneficial for environmental policymaking. In addition, the evaluation and analysis methods offer relevant implications for developing countries.

The purpose of this paper is to examine the role of geographic location of research-intensive firms in the ability to generate new research and products, which consequently affects firm value.

The authors conduct the empirical study following a three-step process. First, if pharmaceutical firms are more likely to cite the patents of other firms and other innovators that are nearby, as opposed to firms and other innovators that are far away, then location (i.e. close proximity) is likely important when it comes to the ability to learn and to use the knowledge being generated by other innovators. The authors employ a “geographic information systems” (GIS) and geo-code each pair of citing and cited patents. In addition, the authors utilize spatial statistics such as Moran’s I to analyze the spatial clustering pattern of patent citations and knowledge flows. Next, the authors measure the pharmaceutical companies’ ability to generate useful patents as a function of the amount of innovation and industrial activity that is occurring close to them. Finally, the authors test whether a firm’s location relates to its firm value. Specifically, the authors model firm value as a function of its patents quality, but the authors also allow the firm’s patents quality to be a function of its location and locational attributes. In this way, the authors establish a link between location and firm value. Using a simultaneous system of equations, the authors find that location explains patent quality, which, in turn, explains firm value. In other words, there is a positive relationship between firm value, innovation and location.

In empirical tests using pharmaceutical firms and their patents, the authors first find that firms more often cite patents of other firms that are geographically closer to them than those firms that are farther away. The authors then find that a patent’s quality is a function of the firm’s near proximity to other knowledge-intensive institutions and activities. Finally, the authors find that because patent quality is a function of a firm’s geographic location, location consequently affects firm value.

For knowledge-intensive firms, geographic location matters. More specifically, the authors contend that research-intensive firms are better able to use and to expand on existing knowledge when they are closer to other research-intensive enterprises. The implication is that firm value maximization involves a location factor.

The practical implication for investors is that investors should invest in those firms that are situated in a location that is rich in geographic innovation resources because those firms are more likely to generate more and higher quality patents or innovations.

The study is the first to establish the linkage among spatial knowledge diffusion, geographic drivers of innovation, and market valuation of the firm. The study is unique in that the authors not only present evidence on spatial knowledge flows by geo-coding the exact longitude and latitude location coordinates of citing and cited patens, but more importantly, the authors also identify geographic drivers of innovation, and examine their impacts on citation-weighted patent counts and knowledge stock. Finally, using a series of simultaneous equations, the authors show how geographic innovation resources positively affect citation-weighted patent stock and knowledge stock and consequently affect market value of the firm. Thus, the novel approach contributes not only to the literature that measures geographic localization of knowledge flow using patent citations, but also to the literature that examines the impact of geographic sources of innovations on patent outputs and patent quality and, thus on firm value for research-intensive firms.

This paper aims to determine if there is a spatial dependence in the entrepreneurial activity among countries. The existence of a “digital proximity” could explain the spatial pattern of entrepreneurship.

This question is empirically addressed by using a five-period, 2008-2012, panel data for 35 countries. A spatial fixed effects panel data model is estimated by using the total entrepreneurial activity published by the global entrepreneurship monitor as the dependent variable.

A significant negative influence of the digital proximity on the entrepreneurial activity is observed. Mobile broadband (MB) direct effect is positive while the indirect effect (the spatial spillovers) is negative, leading to a negative total effect on the total entrepreneurial activity. This result is contrary to non-spatial models’ results. Besides, a higher MB penetration in a country would lead to a competitive advantage fostering its opportunities for entrepreneurship, but reducing those of its neighbours’.

This paper examines the relationship between information and communication technology (ICT) and entrepreneurship, by introducing the spatial effects is the main contribution. This paper expands the scant literature on the ICT impact on entrepreneurship. Results obtained support policies towards enforcing innovation, education and reducing entry regulations for encouraging entrepreneurship. Meanwhile, MB policies could counteract the entrepreneurial policies’ results due to the spatial dependence.

The purpose of this paper is to examine the spatial patterns of gender inequality in junior high school enrollment and the educational resource investments associated with the spatial trends.

The paper uses data on 170 districts in Ghana and hot spot analysis based on the Getis-Ord Gi statistic, linear regression, and geographically weighted regression to assess spatial variability in gender parity in junior high school enrollment and its association with resource allocation.

The results reveal rural-urban and north-south variability in gender parity. Results show that educational resources contribute to gender parity. At the national level, educational expenditure, and the number of classrooms, teachers, and available writing places have the strongest positive associations with girls’ enrollment. These relationships are spatially moderated, such that predominantly rural and Northern districts experience the most substantial benefits of educational investments.

The findings show that strategic allocation of infrastructure, financial, and human resources through local governments holds promise for a more impactful and sustainable educational development of all children, regardless of gender. Besides seeking solutions that address the lack of resources at the national level, there is a need for locally tailored efforts to remove the barriers to equitable distribution of educational resources across gender and socioeconomic groups.

This paper’s use of advanced spatial analysis techniques allows for in-depth examination of gender parity and investments in educational resources, and highlights the spatial nuances in how such investments predict gender disparities in junior high school enrollment. The findings speak to the need for targeted and localized efforts to address gender and geographical disparities in educational opportunities.

This study aims to examine the differences within individuals and clusters in nutritional status and identify socioeconomic factors in the nutritional status of under-five children in Ethiopia.

A weighted sub-sample of 5,270 under-five children was drawn from the under-five children data set of the Ethiopian 2019 Mini Demographic and Health Survey. Multilevel modeling was used to look at the association between the nutritional status of children with predictors.

The proportion of stunting, underweight and wasting among under-five children were 39.3%, 28.6% and 16.3%, respectively. The observed Global Moran Index’s value for child malnutrition (stunting, wasting and underweight) prevalence in Ethiopia were I = 0.204 for stunting, I = 0.152 for wasting and I = 0.195 for underweight at p = 0.000 was statistically significant indicating that spatial variability of malnutrition of under-five children across survey clusters and regions was observed. Moreover, the result of heterogeneity between clusters obtained for stunting, underweight and wasting was significant providing evidence of variation among regional clusters concerning the status of nutrition of under-five children. Child’s age in months, breastfeeding, family educational level, wealth index, place of residence, media access and region were highly significantly associated with childhood malnutrition. The inclusion of the explanatory variables has shown a significant impact on the variation in malnutrition among regions.

Enhance education, expanding the activities regarding nutritional and health services using media, health extension workers, and health institutions.

The study provides the malnutrition situation status of Ethiopian country when the survey carried out.