Conceptualising the demand for ecosystem services – an adapted spatial-structural approach

People require multiple ecosystem services (ES) to meet their basic needs and improve or maintain their quality of life. In order to meet these needs, natural resources are exploited, threatening biodiversity and increasing the pressure on the Earth's ecosystems. Spatial-structural approaches are used to explain and visualise the spatial relationships and connections between areas that provide and benefit from ES. However, areas where the demand for these ES occurs are rarely considered in existing spatial approaches or equated with areas where people can use the benefits. In order to highlight the differences between these two areas, we would like to introduce the 'Service Demanding Area' (SDA) in an adapted spatial-structural approach. This approach relates SDA to already familiar ES provision and use units, namely Service Providing Areas (SPA), Service Connecting Areas (SCA) and Service Benefitting Areas (SBA) and can be used to schematically illustrate, understand and analyse the different forms of demand that can emerge. A literature review was conducted to provide an overview of the spatial mapping of ES demand. Three issues arose that should be addressed to improve the assessment of ES demand: 1) The term ES demand is not used consistently. To avoid confusion, it is important to clarify how ES demand is understood and how it differs from the other components of the ES concept (e.g. ES supply, ES potential, ES flow); 2) It is important to consider that ES demand is multi-faceted and is generated on different geographical scales, including the full range of stakeholders' perceptions, needs and desires which broadens the picture of societal demand for ES; 3) Meaningful interpretations between ES supply and demand need to be available to inform decision-makers about interventions for reducing ES trade-offs and mismatches.

The phenomenon of urban fringe settlements in the south-north region of Malang City

The City of Malang grows annually along with the implementation of its spatial policy. One of the policies that stimulates movement into the city is the development of land for settlements. Limited land and high demands have led to the expansion of settlements towards the urban fringe of Malang City, especially for the south-north region. The purpose of this study was to identify the characteristics of settlements in the north-south regions of Malang City. The variables studied consisted of patterns and types of settlement, land use and land cover, land prices, housing density, and the population. The five aspects of the settlement were studied using the descriptive statistical analysis methods and spatial mapping. The results of descriptive statistical analysis show that there are different characteristics of settlements in the north-south regions of Malang City. The difference can be seen in the four aspects, such as land use and land cover, land prices, housing density, and population. Meanwhile, based on the results of spatial mapping analysis, the different characteristics occurs due to the availability of road access and proximity to city-regional-scale facilities.

A Machine Learning Approach for Spatial Mapping of the Health Risk Associated with Arsenic-Contaminated Groundwater in Taiwan’s Lanyang Plain

Groundwater resources are abundant and widely used in Taiwan’s Lanyang Plain. However, in some places the groundwater arsenic (As) concentrations far exceed the World Health Organization’s standards for drinking water quality. Measurements of the As concentrations in groundwater show considerable spatial variability, which means that the associated risk to human health would also vary from region to region. This study aims to adapt a back-propagation neural network (BPNN) method to carry out more reliable spatial mapping of the As concentrations in the groundwater for comparison with the geostatistical ordinary kriging (OK) method results. Cross validation is performed to evaluate the prediction performance by dividing the As monitoring data into three sets. The cross-validation results show that the average determination coefficients (R2) for the As concentrations obtained with BPNN and OK are 0.55 and 0.49, whereas the average root mean square errors (RMSE) are 0.49 and 0.54, respectively. Given the better prediction performance of the BPNN, it is recommended as a more reliable tool for the spatial mapping of the groundwater As concentration. Subsequently, the As concentrations estimated obtained using the BPNN are applied to develop a spatial map illustrating the risk to human health associated with the ingestion of As-containing groundwater based on the noncarcinogenic hazard quotient (HQ) and carcinogenic target risk (TR) standards established by the U.S. Environmental Protection Agency. Such maps can be used to demarcate the areas where residents are at higher risk due to the ingestion of As-containing groundwater, and prioritize the areas where more intensive monitoring of groundwater quality is required. The spatial mapping of As concentrations from the BPNN was also used to demarcate the regions where the groundwater is suitable for farmland and fishponds based on the water quality standards for As for irrigation and aquaculture.