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

Almost any data can be referenced in geographic space. Such data permit advanced analyses that utilize the position and relationships of objects in space as well as geographic background information. Even though spatial data mining is still a young research discipline, in the past years research advances have shown that the particular challenges of spatial data can be mastered and that the technology is ready for practical application when spatial aspects are treated as an integrated part of data mining and model building. In this chapter in particular, we give a detailed description of several customer projects that we have carried out and which all involve customized data mining solutions for business relevant tasks. The applications range from customer segmentation to the prediction of traffic frequencies and the analysis of GPS trajectories. They have been selected to demonstrate key challenges, to provide advanced solutions and to arouse further research questions.

Spatial Data Mining in Practice: Principles and Case Studies


 With ever more people living in cities worldwide, it becomes increasingly important to understand and improve the impact of the urban habitat on livability, health behaviors and health outcomes. However, implementing interventions that tackle the exposome in complex urban systems can be costly and have long-term, sometimes unforeseen, impacts. Hence, it is crucial to assess the health impact, cost-effectiveness, and social distributional impacts of possible urban exposome interventions before implementing them. Spatial agent-based modeling can capture complex behavior-environment interactions, exposure dynamics, and social outcomes in a spatial context. This paper discusses model architectures and methodological challenges for successfully modeling urban exposome interventions using spatial agent-based modeling. We review the potential and limitations of the method; model components required to capture active and passive exposure and intervention effects; human-environment interactions and their integration into the macro-level health impact assessment and social costs benefit analysis; strategies for model calibration. Major challenges for a successful application of agent-based modeling to urban exposome intervention assessment are (1) the design of realistic behavioral models that can capture different types of exposure and that respond to urban interventions, (2) the mismatch between the possible granularity of exposure estimates and the evidence for corresponding exposure-response functions, (3) the scalability issues that emerge when aiming to estimate long-term effects such as health and social impacts based on high-resolution models of human-environment interactions, (4) as well as the data- and computational complexity of calibrating the resulting agent-based model. Although challenges exist, strategies are proposed to improve the implementation of ABM in exposome research.

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Agent-based Modeling of Urban Exposome Interventions: Prospects, Model Architectures and Methodological Challenges

Data Sovereign Humans and the Information Economy: Towards Design Principles for Human Centric B2C Data Ecosystems

Abstract A significant challenge in bootstrapping a jointly used infrastructure such as Data Spaces is to incentivize the participants to invest in setting up the infrastructure. In this chapter, we investigate this challenge and possible solutions, focusing on an approach called “Tokenomics.” The incentivization scheme should be utilized by governance frameworks, in which the participants of Data Spaces remain capable of action and independent through automated, effective, and fair decision-making processes. Also, potential participants should be motivated to participate in the establishment and further development of the system, while on the other hand, undesirable behavior should be penalized. In combination with distributed ledger technology (DLT) and machine-readable, legally compliant smart contracts, participant behavior can be affected in such a way that both data quality and quantity are improved for the whole Data Space. To derive possible design options for Tokenomics approaches, we examine different token frameworks and their impact on participants. The investigation of the frameworks is carried out taking into account five significant domains: technical, behavior, inherent value, coordination, and pseudo-archetypes. Furthermore, we investigate which token designs provide smaller or larger incentives in order to join or maintain a DLT-based ecosystem. 

/pdf/tokenomics-decentralized-incentivization-in-the-context-of-18wgob9z.pdf

Simon Scheider

Papers

Spatial Data Mining in Practice: Principles and Case Studies

Agent-based Modeling of Urban Exposome Interventions: Prospects, Model Architectures and Methodological Challenges

Data Sovereign Humans and the Information Economy: Towards Design Principles for Human Centric B2C Data Ecosystems

Tokenomics: Decentralized Incentivization in the Context of Data Spaces

Annotating spatio-temporal datasets for meaningful analysis in the Web