Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Machine learning

Statistics for Spatial Data.

The cultural origins of human cognition

Simple features are a standardized way of encoding spatial vector data (points, lines, polygons) in computers. The sf package implements simple features in R, and has roughly the same capacity for spatial vector data as packages sp, rgeos and rgdal. We describe the need for this package, its place in the R package ecosystem, and its potential to connect R to other computer systems. We illustrate this with examples of its use. What are simple features? Features can be thought of as “things” or objects that have a spatial location or extent; they may be physical objects like a building, or social conventions like a political state. Feature geometry refers to the spatial properties (location or extent) of a feature, and can be described by a point, a point set, a linestring, a set of linestrings, a polygon, a set of polygons, or a combination of these. The simple adjective of simple features refers to the property that linestrings and polygons are built from points connected by straight line segments. Features typically also have other properties (temporal properties, color, name, measured quantity), which are called feature attributes. Not all spatial phenomena are easy to represent by “things or objects”: continuous phenoma such as water temperature or elevation are better represented as functions mapping from continuous or sampled space (and time) to values (Scheider et al., 2016), and are often represented by raster data rather than vector (points, lines, polygons) data. Simple feature access (Herring, 2011) is an international standard for representing and encoding spatial data, dominantly represented by point, line and polygon geometries (ISO, 2004). It is widely used e.g. by spatial databases (Herring, 2010), GeoJSON (Butler et al., 2016), GeoSPARQL (Perry and Herring, 2012), and open source libraries that empower the open source geospatial software landscape including GDAL (Warmerdam, 2008), GEOS (GEOS Development Team, 2017) and liblwgeom (a PostGIS component, Obe and Hsu (2015)). The need for a new package Package sf (Pebesma, 2017) is an R package for reading, writing, handling and manipulating simple features in R, reimplementing the vector (points, lines, polygons) data handling functionality of packages sp (Pebesma and Bivand, 2005; Bivand et al., 2013), rgdal (Bivand et al., 2017) and rgeos (Bivand and Rundel, 2017). However, sp has some 400 direct reverse dependencies, and a few thousand indirect ones. Why was there a need to write a package with the potential to replace it? First of all, at the time of writing sp (2003) there was no standard for simple features, and the ESRI shapefile was by far the dominant file format for exchanging vector data. The lack of a clear (open) standard for shapefiles, the omnipresence of “bad” or malformed shapefiles, and the many limitations of the ways it can represent spatial data adversely affected sp, for instance in the way it represents holes in polygons, and a lack of discipline to register holes with their enclosing outer ring. Such ambiguities could influence plotting of data, or communication with other systems or libraries. The simple feature access standard is now widely adopted, but the sp package family has to make assumptions and do conversions to load them into R. This means that you cannot round-trip data, as of: loading data in R, manipulating them, exporting them and getting the same geometries back. With sf, this is no longer a problem. A second reason was that external libraries heavily used by R packages for reading and writing spatial data (GDAL) and for geometrical operations (GEOS) have developed stronger support for the simple feature standard. A third reason was that the package cluster now known as the tidyverse (Wickham, 2017, 2014), which includes popular packages such as dplyr (Wickham et al., 2017) and ggplot2 (Wickham, 2016), does not work well with the spatial classes of sp: • tidyverse packages assume objects not only behave like data.frames (which sp objects do by providing methods), but are data.frames in the sense of being a list with equally sized column vectors, which sp does not do. The R Journal Vol. XX/YY, AAAA 20ZZ ISSN 2073-4859 CONTRIBUTED RESEARCH ARTICLE 2 • attempts to “tidy” polygon objects for plotting with ggplot2 (“fortify”) by creating data.frame objects with records for each polygon node (vertex) were neither robust nor efficient. A simple (S3) way to store geometries in data.frame or similar objects is to put them in a geometry list-column, where each list element contains the geometry object of the corresponding record, or data.frame “row”; this works well with the tidyverse package family.

/pdf/simple-features-for-r-standardized-support-for-spatial-5a1osg0mnp.pdf

Simple Features for R: Standardized Support for Spatial Vector Data

Thirty-three brothels in rural and small-town Nevada, which contain between 225 and 250 prostitutes, are legal or openly tolerated and strictly controlled by state statute, city and county ordinances, and local rules. Twenty-two of the brothels are in places with populations between 500 and 8,000, and the remaining eleven are in rural areas. The legal and quasi-legal restrictions placed on prostitutes severely limit their activities outside brothels. These restrictions in conjunction with historical inertia, perceived benefits of crime and venereal disease control, and the good image of madams contribute to widespread positive local attitudes toward brothel prostitution. Interactions between clients and prostitutes in brothel parlors are also restricted and limited to a few basic types which are largely determined by entrepreneurial philosophy. KEY WORDS : Nevada, Political geography, Prostitution, Restricted activity spaces.

ANNALS of the Association of American Geographers

Interoperability is the main challenge on the way to efficiently find and access spatial data on the web. Significant contributions regarding interoperability have been made by the Open Geospatial Consortium (OGC), where web service standards to publish and download spatial data have been established. The OGCs GeoSPARQL specification targets spatial data on the Web as Linked Open Data (LOD) by providing a comprehensive vocabulary for annotation and querying. While OGC web service standards are widely implemented in Geographic Information Systems (GIS) and offer a seamless service infrastructure, the LOD approach offers structured techniques to interlink and semantically describe spatial information. It is currently not possible to use LOD as a data source for OGC web services. In this chapter we make a suggestion for technically linking OGC web services and LOD as a data source, and we explore and discuss its benefits. We describe and test an adapter that enables access to geographic LOD datasets from within OGC Web Feature Service (WFS), enabling most current GIS to access the Web of Data. We discuss performance tests by comparing the proposed adapter to a reference WFS implementation.

/pdf/making-the-web-of-data-available-via-web-feature-services-abbxlfv0o8.pdf

Making the web of data available via web feature services

Question Answering (QA), the process of computing valid answers to questions formulated in natural language, has recently gained attention in both industry and academia. Translating this idea to th...

https://www.tandfonline.com/doi/pdf/10.1080/17538947.2020.1738568

Geo-Analytical Question-Answering with GIS

Private transport accounts for a large amount of total CO2 emissions, thus significantly contributing to global warming. Tools that actively support people in engaging in a more sustainable life-style without restricting their mobility are urgently needed. How can location-aware information and communication technology (ICT) enable novel interactive and participatory approaches that help people in becoming more sustainable? In this survey paper, we discuss the different aspects of this challenge from a technological and cognitive engineering perspective, based on an overview of the main information processes that may influence mobility behavior. We review the state-of-the-art of research with respect to various ways of influencing mobility behavior (e.g., through providing real-time, user-specific, and location-based feedback) and suggest a corresponding research agenda. We conclude that future research has to focus on reflecting individual goals in providing personal feedback and recommendations that take into account different motivational stages. In addition, a long-term and large-scale empirical evaluation of such tools is necessary.

http://www.goeco-project.ch/wp-content/uploads/downloads/CompTransportScience.pdf

Towards sustainable mobility behavior: research challenges for location-aware information and communication technology

Geospatial semantics as a research field studies how to publish, retrieve, reuse, and integrate geo-data, how to describe geo-data by conceptual models, and how to develop formal specifications on top of data structures to reduce the risk of incompatibilities. Geo-data is highly heterogeneous and ranges from qualitative interviews and thematic maps to satellite imagery and complex simulations. This makes ontologies, semantic annotations, and reasoning support essential ingredients towards a Geospatial Semantic Web. In this paper, we present an overview of major research questions, recent findings, and core literature.

A geo-semantics flyby

We introduce s-kNN, a nearest neighbor based spatial data mining algorithm. It belongs to the class of vector-geometry based algorithms that reason on complex spatial objects instead of point measurements. In contrast to most methods in this class, it does on the fly spatial computations that cannot be replaced by a pre-processing step without sacrificing efficiency. The key is a partial evaluation scheme for efficient computations. The algorithm is fully integrated into an object-relational spatial database. It is the basis for traffic frequency predictions (vehicles and pedestrians) for all German cities larger than 50,000 inhabitants and is the basis for pricing of posters in Germany.

Simon Scheider

Papers

Making the web of data available via web feature services

Geo-Analytical Question-Answering with GIS

Towards sustainable mobility behavior: research challenges for location-aware information and communication technology

A geo-semantics flyby

A Vector-Geometry Based Spatial kNN-Algorithm for Traffic Frequency Predictions