Spatial prediction based on Third Law of Geography

Abstract

Current methods of spatial prediction are based on either the First Law of Geography or the statistical principle or the combination of these two. The Second Law of Geography contributes to the revision of these methods so they are adaptive to local conditions but at the cost of increasing demand for samples. This paper presents a new thinking about spatial prediction based on the Third Law of Geography which focuses on the similarity of geographic configuration of locations. Under the Third Law of Geography, spatial prediction can be made on the basis of the similarity of geographic configurations between a sample and a prediction point. This allows the representativeness of a single sample to be used in prediction. A case study in predicting spatial variation of soil organic matter content was used to compare the spatial prediction based the Third Law of Geography with those based on the First Law and the statistical principle. It is concluded that spatial prediction based on the Third Law of Geography does not require samples to be over certain size nor to be of a particular spatial distribution to achieve a high quality prediction. The prediction uncertainty associated with spatial prediction based on the Third Law of Geography is more indicative to quality of the prediction, thus more effective in allocating error reduction efforts. These properties make spatial prediction based on the Third Law of Geography more suitable for prediction over large and complex geographic areas.

Dynamic Recommendation of Substitute Locations for Inaccessible Soil Samples during Field Sampling Campaign

“…Another issue in the application of the proposed method, as well as other existing DSM-related methods based on soil-environment relationships, is the selection of environmental covariates and the preparation of environmental covariates dataset, which should be different among study areas and is often depended on the domain knowledge [1,16]. When the environmental covariates dataset adopted cannot well characterize the spatial variation of soil in the study area, the performance of the proposed method for recommending substitute locations based on environmental similarity will be impacted.…”

“…Purposive sampling is to design samples based on samples' representativeness of the geographic environment. Sample representativeness is often quantified based on the similarity of environmental conditions between the sample and other locations in the area [4,5,16]. Therefore, for an inaccessible sample from purposive sampling, the substitutive score of a potential substitute location is determined by its environmental similarity to the inaccessible sample.…”

Gap-Filling of a MODIS Normalized Difference Snow Index Product Based on the Similar Pixel Selecting Algorithm: A Case Study on the Qinghai–Tibetan Plateau

“…According to Tobler's first law of geography (TFL) [67,68], nearby objects are more closely related than distant objects. Thus, closer pixels would have more similar attribute values than pixels further apart [71]. When pixel P is covered by the cloud, the deviation degree between the cloud-free pixels near pixel P and the multi-year average NDSI of their corresponding position can theoretically represent, to a certain extent, the deviation degree between the NDSI value of pixel P and its multi-year average.…”

Geospatial semantics, ontology and knowledge graphs for big Earth data

“…The 4th scientific research paradigm, Data-Intensive Scientific Discovery, emphasizes using big data processing and simulation models to mine and analyze massive scientific data to discover scientific laws and problems hidden behind the data (Hey, Tansley, & Tolle, 2009;Zhu et al, 2016). Earth sciences is a typical data-intensive research field that requires not only geospatial big data but also a highly efficient computing infrastructure to support the operation and application of geospatial models and tools that are used to discover spatiotemporal distribution patterns and differentiation rules (Zhu, Lu, Liu, Qin, & Zhou, 2018). Therefore, for Earth sciences, there is an urgent need to develop an one-stop scientific research platform (e-Geoscience) that integrates the sharing of geospatial data, models and computing resources (Zhu et al, 2016).…”