Spatial Models of Population Processes

Spatial theory is divided between models of elections and models of roll call voting, neither of which alone can explain congressional polarization. This chapter discusses the history of spatial theory, why it is important to link the two strands of spatial models, and the value of reversing the order of conventional models. Conventional models place an election before policy decisions are made. This chapter proposes a unified spatial model of Congress in which the conventional order is reversed. First, there is a legislative session, then an election in which voters respond retrospectively, not to the locations candidates claim to hold, but to the bundles of roll call votes that incumbents cast to incrementally adopt their locations in the policy space. Such a model is best suited to explaining three puzzles: why do legislators adopt extreme positions, how do they win, and what role do parties play in the process?

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Spatial Social Choice

The Oxford Handbook of Public Choice, Volume 1 ◽

10.1093/oxfordhb/9780190469733.013.11 ◽

2019 ◽

pp. 207-244

Author(s):

Norman Schofield

Keyword(s):

United States ◽

Stochastic Model ◽

Social Choice ◽

Spatial Models ◽

The United States ◽

Multinomial Logit ◽

Experimental Results ◽

The Core ◽

Voting Game ◽

Voter Choice

A key concept of social choice is the idea of the Condorcet point or core. For example, consider a voting game with four participants so any three will win. If voters have Euclidean preferences, then the point at the center will be unbeaten. Earlier spatial models of social choice focused on deterministic voter choice. However, it is clear that voter choice is intrinsically stochastic. This chapter employs a stochastic model based on multinomial logit to examine whether parties in electoral competition tend to converge toward the electoral center or respond to activist pressure to adopt more polarized policies. The chapter discusses experimental results of the idea of the core explores empirical analyses of elections in Israel and the United States.

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Mitogenomic analysis of diversity of key whitefly pests in Kenya and its implication to their sustainable management

Scientific Reports ◽

10.1038/s41598-021-85902-2 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Fathiya M. Khamis ◽

Fidelis L. O. Ombura ◽

Inusa J. Ajene ◽

Komivi S. Akutse ◽

Sevgan Subramanian ◽

...

Keyword(s):

Sustainable Management ◽

Management Strategies ◽

Spatial Models ◽

Abundant Species ◽

Plant Viruses ◽

Pest Species ◽

Agricultural Pests ◽

Protein Coding ◽

Aleyrodes Proletella ◽

Aleurodicus Dispersus

AbstractWhiteflies (Hemiptera: Aleyrodidae) are devastating agricultural pests of economic importance vectoring pathogenic plant viruses. Knowledge on their diversity and distribution in Kenya is scanty, limiting development of effective sustainable management strategies. The present study is aimed at identifying whitefly pest species present in Kenya across different agroecological zones and establish predictive models for the most abundant species in Africa. Whiteflies were sampled in Kenya from key crops known to be severely infested and identified using 16S rRNA markers and complete mitochondrial genomes. Four whitefly species were identified: Aleyrodes proletella, Aleurodicus dispersus, Bemisia afer and Trialeurodesvaporariorum, the latter being the most dominant species across all the agroecology. The assembly of complete mitogenomes and comparative analysis of all 13 protein coding genes confirmed the identities of the four species. Furthermore, prediction spatial models indicated high climatic suitability of T. vaporariorum in Africa, Europe, Central America, parts of Southern America, parts of Australia, New Zealand and Asia. Consequently, our findings provide information to guide biosecurity agencies on protocols to be adopted for precise identification of pest whitefly species in Kenya to serve as an early warning tool against T. vaporariorum invasion into unaffected areas and guide appropriate decision-making on their management.

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An ABC-optimized fuzzy ELECTRE approach for assessing petroleum potential at the petroleum system level

Open Geosciences ◽

10.1515/geo-2020-0159 ◽

2020 ◽

Vol 12 (1) ◽

pp. 580-597

Author(s):

Mohamad Hamzeh ◽

Farid Karimipour

Keyword(s):

Spatial Data ◽

Spatial Models ◽

Model Performance ◽

Essential Elements ◽

Petroleum System ◽

Petroleum Exploration ◽

System Level ◽

Data Sets ◽

Petroleum Potential ◽

Spatial Data Sets

AbstractAn inevitable aspect of modern petroleum exploration is the simultaneous consideration of large, complex, and disparate spatial data sets. In this context, the present article proposes the optimized fuzzy ELECTRE (OFE) approach based on combining the artificial bee colony (ABC) optimization algorithm, fuzzy logic, and an outranking method to assess petroleum potential at the petroleum system level in a spatial framework using experts’ knowledge and the information available in the discovered petroleum accumulations simultaneously. It uses the characteristics of the essential elements of a petroleum system as key criteria. To demonstrate the approach, a case study was conducted on the Red River petroleum system of the Williston Basin. Having completed the assorted preprocessing steps, eight spatial data sets associated with the criteria were integrated using the OFE to produce a map that makes it possible to delineate the areas with the highest petroleum potential and the lowest risk for further exploratory investigations. The success and prediction rate curves were used to measure the performance of the model. Both success and prediction accuracies lie in the range of 80–90%, indicating an excellent model performance. Considering the five-class petroleum potential, the proposed approach outperforms the spatial models used in the previous studies. In addition, comparing the results of the FE and OFE indicated that the optimization of the weights by the ABC algorithm has improved accuracy by approximately 15%, namely, a relatively higher success rate and lower risk in petroleum exploration.

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Spatial analysis for political scientists

Italian Political Science Review/Rivista Italiana di Scienza Politica ◽

10.1017/ipo.2021.7 ◽

2021 ◽

pp. 1-17

Author(s):

Jessica Di Salvatore ◽

Andrea Ruggeri

Keyword(s):

Comparative Politics ◽

Spatial Data ◽

Spatial Clustering ◽

Statistical Tests ◽

Spatial Regression ◽

Spatial Models ◽

Building Blocks ◽

Spatial Relationships ◽

Electoral Studies ◽

Data Points

Abstract How does space matter in our analyses? How can we evaluate diffusion of phenomena or interdependence among units? How biased can our analysis be if we do not consider spatial relationships? All the above questions are critical theoretical and empirical issues for political scientists belonging to several subfields from Electoral Studies to Comparative Politics, and also for International Relations. In this special issue on methods, our paper introduces political scientists to conceptualizing interdependence between units and how to empirically model these interdependencies using spatial regression. First, the paper presents the building blocks of any feature of spatial data (points, polygons, and raster) and the task of georeferencing. Second, the paper discusses what a spatial matrix (W) is, its varieties and the assumptions we make when choosing one. Third, the paper introduces how to investigate spatial clustering through visualizations (e.g. maps) as well as statistical tests (e.g. Moran's index). Fourth and finally, the paper explains how to model spatial relationships that are of substantive interest to some of our research questions. We conclude by inviting researchers to carefully consider space in their analysis and to reflect on the need, or the lack thereof, to use spatial models.

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