Robustness of Maximin Space-filling Designs

In this report, we first have a review of the maximin space-filling design methods that is often applied and discussed in the literature (for example, Müller (2007)). Then we will discuss the robustness of the maximin space-filling design against model misspecification via numerical simulation. For this purpose, we will generate spatial data sets on a n x n grid and design points are selected from the n2 locations. The predictions at the unsampled locations are made based on the observations at these design points. Then the mean of the squared prediction errors are estimated as a measure of the robustness of the designs against possible model misspecification. Surprisingly, according to the simulation results, we find that the maximin space-filling designs may be robust against possible model misspecification in the sense that the mean of the squared prediction error does not increase significantly when the model is misspecified. Although the results were obtained based on simple models, this result is very inspiring. It will guide further numerical and theoretical studies which will be done as future work.

The Lyme and Tickborne Disease Dashboard: A map-based resource to promote public health awareness and research collaboration

With the incidence of Lyme and other tickborne diseases on the rise in the US and globally, there is a critical need for data-driven tools that communicate the magnitude of this problem and help guide public health responses. We present the Johns Hopkins Lyme and Tickborne Disease Dashboard (https://www.hopkinslymetracker.org/), a new tool that harnesses the power of geography to raise awareness and fuel research and scientific collaboration. The dashboard is unique in applying a geographic lens to tickborne diseases, aiming not only to become a global tracker of tickborne diseases but also to contextualize their complicated geography with a comprehensive set of maps and spatial data sets representing a One Health approach. We share our experience designing and implementing the dashboard, describe the main features, and discuss current limitations and future directions.

Content standards for geospatial metadata and their use

*-2mm Among the many GIS standards that provide interoperability of (geo)spatial data and related web services, we can identify a limited but important group of standards, intended to catalog spatial data sets and services. Many of currently used standards are based on international ISO 19115 series and their national profiles. Among them are two Russian national standards developed by the Technical Committee (TC) 394 Geographic information/Geomatics of the Federal Agency on Technical Regulating and Metrology (Rosstandart): the GOST R 57668-2017 “Spatial data. Metadata. Part 1. Fundamentals” and the GOST R 57656-2017 “Spatial data. Metadata. Part 2. Extensions for imagery and gridded data”. The analysis of Russian, foreign and international geoportals with metadata editing, validation and publishing functions has been carried out, including using ISO 19115, FGDC-STD-001-001-1998, DIF, Dublin Core and open source software GeoNode, GeoNetwork, GeoServer, etc. The results of the analysis can be useful in selecting effective spatial metadata management systems in the scientific geoportals.

The Highest Peaks of the Mountains: Comparing the Use of GNSS, LiDAR Point Clouds, DTMs, Databases, Maps, and Historical Sources

Advances in remote data acquisition techniques have contributed to the flooding of society with spatial data sets and information. Widely available spatial data sets, including digital terrain models (DTMs) from aerial laser scanning (ALS) data, are finding more and more new applications. The article analyses and compares the heights of the 14 highest peaks of the Polish Carpathians derived from different data sources. Global navigation satellite system (GNSS) geodetic measurements were used as reference. The comparison primarily involves ALS data, and selected peaks’ GNSS measurements carried out with Xiaomi Mi 8 smartphones were also compared. Recorded raw smartphone GNSS measurements were used for calculations in post-processing mode. Other data sources were, among others, global and local databases and models and topographic maps (modern and old). The article presents an in-depth comparison of Polish and Slovak point clouds for two peaks. The results indicate the possible use of large-area laser scanning in determining the maximum heights of mountain peaks and the need to use geodetic GNSS measurements for selected peaks. For the Polish peak of Rysy, the incorrect classification of point clouds causes its height to be overestimated. The conclusions presented in the article can be used in the dissemination of knowledge and to improve positioning methods.

A generic test for the similarity of spatial data

Two spatial data sets are considered to be similar if they originate from the same stochastic process in terms of their spatial structure. Many tests have been developed over recent years to test the similarity of certain types of spatial data, such as spatial point patterns, geostatistical data and images. This research proposes a generic spatial similarity test able to handle various types of spatial data, for example images (modelled spatially), point patterns, marked point patterns, geostatistical data and lattice patterns. A simulation study is done in order to test the method for each spatial data set. After the simulation study, it was concluded that the proposed spatial similarity test is not sensitive to the user-defined resolution of the pixel image representation. From the simulation study, the proposed spatial similarity test performs well on lattice data, some of the unmarked point patterns and the marked point patterns with discrete marks. We illustrate this test on property prices in the City of Cape Town and the City of Johannesburg, South Africa.

Improving GIS-Based Heat Demand Modelling and Mapping for Residential Buildings with Census Data Sets at Regional and Sub-Regional Scales

Heat demand of buildings and related CO2 emissions caused by energy supply contribute to global climate change. Spatial data-based heat planning enables municipalities to reorganize local heating sectors towards efficient use of regional renewable energy resources. Here, annual heat demand of residential buildings is modeled and mapped for a German federal state to provide regional basic data. Using a 3D building stock model and standard values of building-type-specific heat demand from a regional building typology in a Geographic Information Systems (GIS)-based bottom-up approach, a first base reference is modeled. Two spatial data sets with information on the construction period of residential buildings, aggregated on municipality sections and hectare grid cells, are used to show how census-based spatial data sets can enhance the approach. Partial results from all three models are validated against reported regional data on heat demand as well as against gas consumption of a municipality. All three models overestimate reported heat demand on regional levels by 16% to 19%, but underestimate demand by up to 8% on city levels. Using the hectare grid cells data set leads to best prediction accuracy values at municipality section level, showing the benefit of integrating this high detailed spatial data set on building age.

TRANSFORMATION OF SPATIAL DATA INTO THE STATE GEODETIC COORDINATE SYSTEM OF 2011 IN GIS SOFTWARE

The article describes the implementation of the coordinate transformation procedures in GIS (on the example MapInfo Professional) between the existing in the country coordinate systems (SC-42, SC-95, MCS, based on them) and SCS-2011, allowing the reader to learn how to calculate the parame-ters of Helmert transformations between these coordinate systems using GOST 32453-2017. The article notes the problem of the transformation accuracy on the global parameters established by GOST 32453-2017 and the resulting need to determine local versions of these parameters, leading to the creation of uncoordinated spatial data sets in GSK-2011, additional costs and complicating work of consumers. In order to solve this problem, it is proposed to consider the formulation of the task of transition to the implementation of cadastral work from coordinate systems based on SС-42 to MСS, created at SCS-2011, as an actual problem of improving the geodetic support of the country

Big LIDAR Data in Digital Earth: Ways Out of Dead End

During past 25 years, laser scanning has evolved from an experimental method into a fully autonomous family of Earth remote sensing methods. Now this group of methods provides the most accurate and detailed spatial data sets, while the cost of data is constantly falling, the number of measuring instruments (laser scanners) is constantly growing. The volumes of data that will be obtained during the surveys in the coming decades will allow the creation of the first sub-global coverage of the planet. However, the flip side of high accuracy and detail is the need to store fantastically large volumes of three-dimensional data without loss of accuracy. At the same time, the ability to work with the specified data in both 2D and 3D mode should be improved. Standard storage methods (file method, geodatabases, archiving, etc) solve the problem only partially. At the same time, there are some other alternative methods that can remove current restrictions and lead to the emergence of more flexible and functional spatial data infrastructures. One of the most flexible and promising ways of laser data storage and processing are quadtree and octree-based approaches. Of course, these approaches are more complicated than typical file data structures, that are commonly used for LIDAR data storage, but they allow users to solve some typical negative features of point datasets (processing speed, non-topological spatial structure, limited precision, etc.).

The Coupling Co-Location Pattern: A New Spatial Pattern for Spatial Data Sets

There is a variety of interesting knowledge in spatial data sets. Spatial co-location pattern mining can discover sets of different features that are co-located. However, this type of pattern only lists the features that appear together without any consideration of the quantity ratio, which can cause confusion. For example, the co-location pattern church, restaurants shows that churches and restaurants are often close to each other, but information such as how many restaurants are near a church is usually not displayed. Also, in real spatial data sets, there is a mutual influence between spatial features, that is, a coupling relationship between different features or the same features. Thus, this paper proposes a novel spatial pattern called a coupling co-location pattern. First, we discuss the properties of the coupling phenomenon between spatial features, and then the concept of coupling co-location patterns is defined formally. Second, the measurement of support and mining framework for coupling co-location patterns are proposed. Finally, we conduct experiments on both real and synthetic data sets, and the results verify the practical significance of coupling co-location patterns.