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Migration is a major engine of demographic change for towns, regions, and whole countries. Traditionally, migrations have been divided into those caused by push or pull factors or classified by events that triggered them. In other words, push and pull factors are but one way to classify migration movements. This work distinguishes between life course and structural migrations. Life course migrations are linked to important events in people's lives such as graduation, marriage, having children, job promotions, discharge from the military, or retirement and the motivation comes from the migrant. Structural migrations are caused by changes in the economic, social, political, or natural environment and, therefore, externally motivated. This classification scheme is compatible with and complementary to existing classifications and particularly those based on push and pull factors. It yields some additional insights for policy making, empirical research, and migration modeling. [ABSTRACT FROM AUTHOR]

This research examines the effect of participants' expectations on their satisfaction regarding community development planning programs. The marketing literature establishes that satisfaction depends on whether experiences meet or exceed expectations, [...]

This paper examines the relationship between quality of life, rural development, and several socioeconomic variables. The analysis utilizes data obtained from a survey questionnaire administered to a random sample of more than 2,000 residents in West Virginia, and spatial data obtained by geocoding the survey respondents' addressees. Quality of life is measured by a three-point categorical measure of overall satisfaction, and development is measured by a goods and services availability index. A simultaneous ordered probit model is used to examine the relationships. The empirical results are consistent with the theoretical predictions and indicate a simultaneous relationship between quality of life satisfaction and rural development.

Deconcentration, counter-urbanization and trend reversal are three terms used interchangeably in population distribution studies. These three terms are defined and applied in a population distributionstudy of Switzerland from 1900 through 1980. Based on the definitions of these terms, deconcentration do occur in Switzerland but there is weak evidence for counter-urbanization. Trend-reversal fails to give significant proof of its existence in the country.

Immigrants have transformed Miami from a resort town for vacationers from the northeast of the United States into a multicultural metropolitan region. Portes and Stepick have studied this transformation, which [...]

The relationship between quality of life, health, and several socioeconomic variables are examined. The results support the notion that inferences drawn regarding substantive issues often change when spatial dependence is taken in to account.

The Smart Grid incorporates advanced information and communication technologies (ICTs) in power systems, and is characterized by high penetration of distributed energy resources (DERs). Whether it is the nation-wide power grid or a single residential building, the energy management involves different types of resources that often depend on and influence each other. The concept of virtual power plant (VPP) has been proposed to represent the aggregation of energy resources in the electricity market, and distributed decision-making (DDM) plays a vital role in VPP due to its complex nature. This paper proposes a framework for managing different resource types of relevance to energy management for decentralized VPP. The framework views VPP as a hierarchical structure and abstracts energy consumption/generation as contractual resources, i.e., contractual offerings to curtail load/supply energy, from third party VPP participants for DDM. The proposed resource models, event-based approach to decision making, multi-agent system and ontology implementation of the framework are presented in detail. The effectiveness of the proposed framework is then demonstrated through an application to a simulated campus VPP with real building energy data. [ABSTRACT FROM AUTHOR]