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Satellite-based weather index insurance has recently been considered in order to reduce the high basis risk of station-based weather index insurance. However, the use of satellite data with a relatively low spatial resolution has not yet made it possible to determine the satellite indices free of disturbing landscape elements such as mountains, forests and lakes.

In this context, the Normalized Difference Vegetation Index (NDVI) was used based on both Moderate Resolution Imaging Spectroradiometer (MODIS) (250 × 250 m) and high-resolution Landsat 5/8 (30 × 30 m) images to investigate the effect of a higher spatial resolution of satellite-based weather index contracts for hedging winter wheat yields. For three farms in north-east Germany, insurance contracts both at field and farm level were designed.

The results indicate that with an increasing spatial resolution of satellite data, the basis risk of satellite-based weather index insurance contracts can be reduced. However, the results also show that the design of NDVI-based insurance contracts at farm level also reduces the basis risk compared to field level. The study shows that higher-resolution satellite data are advantageous, whereas satellite indices at field level do not reduce the basis risk.

To the best of the author’s knowledge, the effect of increasing spatial resolution of satellite images for satellite-based weather index insurance is investigated for the first time at the field level compared to the farm level.

The purpose of this paper is to present a spatial decision support system (SDSS) to be used by the local authorities of a city in the planning and response phase of a disaster. The SDSS focuses on the management of public spaces as a resource to increase a vulnerable population’s accessibility to essential goods and services. Using a web-based platform, the SDSS would support data-driven decisions, especially for cases such as the COVID-19 pandemic which requires special care in quarantine situations (which imply walking access instead of by other means of transport).

This paper proposes a methodology to create a web-SDSS to manage public spaces in the planning and response phase of a disaster to increase the access to essential goods and services. Using a regular polygon grid, a city is partitioned into spatial units that aggregate spatial data from open and proprietary sources. The polygon grid is then used to compute accessibility, vulnerability and population density indicators using spatial analysis. Finally, a facility location problem is formulated and solved to provide decision-makers with an adaptive selection of public spaces given their indicators of choice.

The design and implementation of the methodology resulted in a granular representation of the city of Lima, Peru, in terms of population density, accessibility and vulnerability. Using these indicators, the SDSS was deployed as a web application that allowed decision-makers to explore different solutions to a facility location model within their districts, as well as visualizing the indicators computed for the hexagons that covered the district’s area. By performing tests with different local authorities, improvements were suggested to support a more general set of decisions and the key indicators to use in the SDSS were determined.

This paper, following the literature gap, is the first of its kind that presents an SDSS focused on increasing access to essential goods and services using public spaces and has had a successful response from local authorities with different backgrounds regarding the integration into their decision-making process.

This study aims to explore the spatial distribution characteristics and spactial reciprocity between industrial parks (IPs) and vocational education parks (VEPs): agglomeration density, functional matching, spatial organization efficiency, as well as space intensive utility. To achieve this objective, IPs and VEPs in urban centers of Jiangsu Province are selected as the objects of the study. First, spatial analysis of thermodynamic diagrams is employed in this study to qualitatively analyze the evolutionary characteristics of the spatial distribution of IPs and VEPs to explore the spatial aggregation characteristics of their clustering, integration, and comprehensive crossover. Second, a horizontal comparison of the data and indexes concerned reveals that areas with high agglomeration and functional matching exhibit a sound spatial reciprocity. Third, this study crystallizes the four structural prototpye paradigms formed during the reciprocity evolution between IPs and VEPs; it compares spatial organization efficiency, with the agglomeration–core structure ranking first, followed by the circle–core structure. Finally, SPSS is used to analyze the space intensive utility in order to verify the conclusions of qualitative analysis. The findings can comprehensively explain the regularities of the spatial distribution and reciprocity between IPs and VEPs. The findngs can also elucidate the design of regional industrial development and educational programs.

The purpose of this paper is to identify the drivers of residential price as well as the degree co-movement of housing among different states in Malaysia.

This study adopted an advanced econometrics technique: the dynamic autoregressive-distributed lag (DARDL) and – the time-frequency domain approach known as the wavelet coherence test. The DARDL model was applied to identify the cointegrating relationships and the CWT was used to analyze the co-movement and lead–lag relationships among four states’ regional housing prices. The extracted data were mainly on annual basis and comprised macroeconomics and financial factors. Information with regard to residential prices and other variables was extracted from the National Property Information Centre (NAPIC) website, the Central Bank of Malaysia Statistics Report, the Department of Statistics, Malaysia, I-Property.com and the World Bank (WB). The data covered in this study were the pool data from four main states in Malaysia and different categories of residential properties.

The empirical results indicate that there were long-run cointegration relationships between the housing price and capital gain and loss, rental per square feet, disposable income, inflation, number of marriages, deposit rate, risk premium and loan-to-value (LTV) ratio. While the wavelet analysis shows that (1) in the long run, Kuala Lumpur housing price having strong co-movement with Selangor, Penang and Melaka housing prices except for Johor and (2) the lead–lag relationship also postulates Kuala Lumpur housing price having in-phase category with Selangor, Penang and Melaka housing prices except for Johor.

This study offers relevant practical implications. First, the study proposes an active collaboration between the private sector and government support which may help to smooth the pricing issue of residential properties. More low-cost residential projects are needed for focus groups including middle- and low-income earners. Furthermore, the results are expected to provide real estate investor in Malaysia, an improved understanding of the regional housing market price dynamics.

The findings of this study were obtained from various reliable sources; therefore, the results reflected the analysis of price drivers and co-movements. Furthermore, findings from this study lend some support to the argument on the rise of residential prices and offer several policy implications from a practical point of view with regard to the residential market.

Large/stochastic spatiotemporal demand data sets can prove intractable for location optimization problems, motivating the need for aggregation. However, demand aggregation induces errors. Significant theoretical research has been performed related to the modifiable areal unit problem and the zone definition problem. Minimal research has been accomplished related to the specific issues inherent to spatiotemporal demand data, such as search and rescue (SAR) data. This study provides a quantitative comparison of various aggregation methodologies and their relation to distance and volume based aggregation errors.

This paper introduces and applies a framework for comparing both deterministic and stochastic aggregation methods using distance- and volume-based aggregation error metrics. This paper additionally applies weighted versions of these metrics to account for the reality that demand events are nonhomogeneous. These metrics are applied to a large, highly variable, spatiotemporal demand data set of SAR events in the Pacific Ocean. Comparisons using these metrics are conducted between six quadrat aggregations of varying scales and two zonal distribution models using hierarchical clustering.

As quadrat fidelity increases the distance-based aggregation error decreases, while the two deliberate zonal approaches further reduce this error while using fewer zones. However, the higher fidelity aggregations detrimentally affect volume error. Additionally, by splitting the SAR data set into training and test sets this paper shows the stochastic zonal distribution aggregation method is effective at simulating actual future demands.

This study indicates no singular best aggregation method exists, by quantifying trade-offs in aggregation-induced errors practitioners can utilize the method that minimizes errors most relevant to their study. Study also quantifies the ability of a stochastic zonal distribution method to effectively simulate future demand data.

This paper develops a new decomposition method of the housing market variations to analyse the housing dynamics of the Australian eight capital cities.

This study reviews the prior research on analysing the housing market variations and classifies the previous methods into four main models. Based on this, the study develops a new decomposition of the variations, which is made up of regional information, home‐market information and time information. The panel data regression method, unit root test and F test are adopted to construct the model and interpret the housing market variations of the Australian capital cities.

This paper suggests that the Australian home‐market information has the same elasticity to the housing market variations across cities and time. In contrast, the elasticities of the regional information are distinguished. However, similarities exit in the west and north of Australia or the south and east of Australia. The time information contributes differently along the observing period, although the similarities are found in certain periods.

This paper introduces the housing market variation decomposition into the research of housing market variations and develops a model based on the new method of the housing market variation decomposition.

This paper aims to study to what extent an insurance based on a house price index provides equity protection for homeowners.

The paper uses a novel dataset of all housing market transactions in the metropolitan area of Melbourne 1990‐2006, to construct repeated sales indices of various temporal spatial aggregation. These indices are used to discuss the efficiency of index‐based insurance schemes. The paper also considers efficiency under different specifications of legitimate claims.

It is found that the payout efficiency is surprisingly stable (around 50 percent) for all temporal spatial aggregations. A neighborhood index outperforms the metropolitan index with respect to target efficiency (the probability of payout given a loss). The introduction of maturity times, say legitimate claim five years after purchase, does improve efficiency somewhat. However, the idiosyncratic component of housing market transactions remains high, and the insurance probably unattractive from a homeowner perspective.

To the authors' knowledge, this is the first time an index‐based insurance scheme is analyzed using real‐market transactions.

To offer a realistic foundation for urban cultural construction planning, we want to investigate the distribution features of Shanghai's cultural functional elements and examine the distribution patterns in urban space.

In this research, we managed to gather POI geographic data, refined and categorized them to integrate eight categories of cultural functional elements, observed the density and agglomeration, distribution direction and hot and cold spots of overall and each type of cultural functional elements using geospatial analysis methods and then investigated the factors influencing cultural functional elements using geographic detectors.

Our research shows apparent differences between regions and most cultural functional elements are found in the inner city. Second, there are hot and cold spots in the way different cultural functional elements are spread out. Its geographic structure is primarily influenced by third-party traffic service capacity and available time.

This work provides a benchmark for cultural planning in Shanghai by establishing the spatial aggregation impact of cultural functional elements.

This study aimed to examine organizational digitalization of farmers' associations (FAs) by conducting internal and external analyses of the contextual and geo-spatial attributes of the organizations, and further explored the role such associations play as key change agents in activating agricultural innovations and development in Taiwan.

Unlike previous digitalization studies, which employed user data such as attitudes and behaviors as the major determinants, the present study focused on the spatial and organizational characteristics of FAs. An exploratory spatial analysis was conducted using a geographic information system to discover whether the organizational characteristics, local agriculture development, and neighboring FAs affect the organizational decision and adaption on information services. Research data from three national surveys were analyzed with a spatial regression model.

Results showed that geographical neighborhood, in association with organizational characteristics, significantly affects the decision and level of organizational digitalization, while the development and characteristics of regional agriculture exert no significant influence.

In the development of information and communication technology (ICT) diffusion policies for promoting organizational digitalization, it is important to highlight regional initiatives and complementary public efforts to guarantee an equitable access to ICT in areas with variances in economic development. In neighborhood FAs, greater development of economic and labor scales and ICT human capital also contribute to higher levels of organizational digitalization.

Based on the study results, suggestions are provided for future research considering spatial forces in agricultural information studies and practical applications.