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

Impatti, adattamento e vulnerabilita Le cause e le responsabilita dei cambiamenti climatici sono state trattate sul numero di ottobre della rivista Cda. Approfondiamo l’argomento presentando il documento: “Cambiamenti climatici 2007: impatti, adattamento e vulnerabilita” votato ad aprile 2007 dal secondo gruppo di lavoro del Comitato Intergovernativo sui Cambiamenti Climatici (Intergovernmental Panel on Climate Change). Si tratta del secondo di tre documenti che compongono il quarto rapporto sui cambiamenti climatici.

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Climate Change 2007: Impacts, Adaptation and Vulnerability.

National Science Education Standards

Transactions of the Institute of British Geographers

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A Break in the Clouds: Towards a Cloud Definition

Public participation geographic information systems (PPGIS) pertains to the use of geographic information systems (GIS) to broaden public involvement in policymaking as well as to the value of GIS to promote the goals of nongovernmental organizations, grassroots groups, and community-based organizations. The article first traces the social history of PPGIS. It then argues that PPGIS has been socially constructed by a broad set of actors in research across disciplines and in practice across sectors. This produced and reproduced concept is then explicated through four major themes found across the breadth of the PPGIS literature: place and people, technology and data, process, and outcome and evaluation. The themes constitute a framework for evaluating current PPGIS activities and a roadmap for future PPGIS research and practice.

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Public Participation Geographic Information Systems: A Literature Review and Framework

Civic open data at a crossroads: Dominant models and current challenges

This paper proposes a new framework to understand the use and value of GIS by social movement groups and to transcend the contested debate over how and whether GIS should be adopted. It frames the ...

Conforming (to) the opposition: the social construction of geographical information systems in social movements

Governments have long been active online, providing services and information to citizens. With the development of Web 2.0 technology, many governments are considering how they can better engage with and accept citizen input online, particularly through the gathering and use of volunteered geographic information (VGI). Though there are several benefits to governments accepting VGI, the process of adopting VGI as a support to decision-making is not without challenge. We identify three areas of challenge to the adoption of VGI by government; these are the costs of VGI, the challenges for governments to accept non-expert data of questionable accuracy and formality, and the jurisdictional issues in VGI. We then identify three ways that governments can situate themselves to accept VGI—by formalizing the VGI collection process, through encouraging collaboration between levels of government, and by investigating the participatory potential of VGI.

Situating the Adoption of VGI by Government

Increasing numbers of activist non-profits and traditionally marginalized peoples are adopting gis as a tool for social change. Its use is scrutinized by academics who worry that gis embodies a mechanism for misrepresentation, diversion, control, and surveillance. This critique has not slowed adoption, and for the past decade researchers have been investigating the use and value of gis in a variety of non-profits and citizens' groups. Many of these new users are calling for a different kind of gis — a gis/2. This gis must be able to represent different measures and visions of place and integrate local knowledge, support cultural and multi-lingual distinctions, and preserve — rather than reduce — friction, disagreement, redundancy, and even error.In this paper I argue that one must "rewire gis" — that is, engage the code and the coding directly — to build a gis/2. A literature review on the use and value of gis in social movements, activist non-profits, and citizens' groups illustrates the limitations of c...

Renee Sieber

Papers

Public Participation Geographic Information Systems: A Literature Review and Framework

Civic open data at a crossroads: Dominant models and current challenges

Conforming (to) the opposition: the social construction of geographical information systems in social movements

Situating the Adoption of VGI by Government

Rewiring for a GIS/2