Analysing Interaction Data

2010 ◽  
pp. 69-88
Author(s):  
John Stillwell ◽  
Kirk Harland
Keyword(s):  
Spatial Interaction ◽  
Interaction Model ◽  
Data Sets ◽  
Interaction Data ◽  
Interaction Variables ◽  
Movement Function ◽  
Impossible Task ◽  
Log Linear ◽  
Calibration Techniques ◽  
Modelling Methods

Large and complex interaction data sets present researchers with analytical challenges and this chapter attempts to identify and illustrate a number of ways to analyse origin-destination flows. Given the impossible task of providing a comprehensive review in such a limited space, certain analytical measures, modelling methods and visualisation techniques have been selected for inclusion, following an introduction to the notation commonly employed to represent interaction variables. Various Census and NHS patient register data sets are used to exemplify interaction measures, beginning with simple net balances and inflow/outflow ratios and moving onto indices of connectivity, inequality and distance moved. The multiplicative component framework is introduced as a particularly useful analytical approach. More sophisticated methods of modelling interaction data using statistical or mathematical calibration techniques are reviewed, examples of log-linear regression and spatial interaction model structure are highlighted in the context of historical calibration and a brief discussion of the use models for future projection is included. Maps that show patterns of geographical movement function as effective illustrative and research tools. Computerized mapping of geographical movement has evolved since the 1970s and 1980s and, in this chapter, we introduce a new method of mapping flows using vectors and illustrate this approach with micro data on pupils travelling to school. The chapter aims to provide a broad introduction to analysis methods for interaction data, many of which are subsequently applied in later chapters of the book.

scholarly journals A new framework for very large-scale urban modelling

Urban Studies ◽  
2021 ◽  
pp. 004209802098225
Author(s):  
Michael Batty ◽  
Richard Milton
Keyword(s):  
Large Scale ◽  
Vector Fields ◽  
Spatial Interaction ◽  
Interaction Model ◽  
Prototype Model ◽  
Data Sets ◽  
Spatial Interactions ◽  
Model Framework ◽  
The Impact ◽  
New Framework

The generation of ever-bigger data sets pertaining to the distribution of activities in cities is paralleled by massive increases in computer power and memory that are enabling very large-scale urban models to be constructed. Here we present an effort to extend traditional land use–transport interaction (LUTI) models to extensive spatial systems so that they are able to track increasingly wide repercussions on the location of population, employment and related distributions of spatial interactions. The prototype model framework we propose and implement called QUANT is available anywhere, at any time, at any place, and is open to any user. It is characterised as a set of web-based services within which simulation, visualisation and scenario generation are configured. We begin by presenting the core spatial interaction model built around the journey to work, and extend this to deal with many sectors. We detail the computational environment, with a focus on the size of the problem which is an application to a 8436 zone system comprising England, Scotland and Wales generating matrices of around 71 million cells. We detail the data and spatial system, showing how we extend the model to visualise spatial interactions as vector fields and accessibility indicators. We briefly demonstrate the implementation of the model and outline how we can generate the impact of changes in employment and changes in travel costs that enable transport modes to compete for travellers. We conclude by indicating that the power of the new framework consists of running hundreds of ‘what if?’ scenarios which let the user immediately evaluate their impacts and then evolve new and better ones.

Functional Regionalization of Spatial Interaction Data: A Comment

1981 ◽  
Vol 13 (5) ◽  
pp. 645-646 ◽  
Author(s):  
A Findlay ◽  
P B Slater
Keyword(s):  
Spatial Interaction ◽  
Interaction Data ◽  
Functional Regionalization ◽  
Independent Effects

A recent paper by Masser and Scheurwater (1980) favoured the adoption of the intramax procedure for functional regionalization, without satisfactorily investigating the independent effects of the approach of the procedure to standardization and to clustering. From an examination of these phases of the intramax procedure it is shown that the superiority of Masser and Scheurwater's approach is based on doubtful criteria.

Comments on the Urban Spatial-Interaction Model Based on the Intervening-Opportunities Principle

1979 ◽  
Vol 11 (5) ◽  
pp. 527-539 ◽  
Author(s):  
Irena Chudzyńska ◽  
Z Słodkowski
Keyword(s):  
Mathematical Model ◽  
Spatial Interaction ◽  
Interaction Model ◽  
Spatial Interaction Model ◽  
Model Based ◽  
Number Of Equilibria ◽  
Intervening Opportunities

A mathematical model of urban spatial interaction based on the intervening-opportunities principle is discussed and its equilibria are studied. It is shown that, under natural assumptions, the number of equilibria is finite, and a mathematical criterion for distinguishing the equilibrium corresponding to reality is given.

scholarly journals WONKAandOOMMPPAA: analysis of protein–ligand interaction data to direct structure-based drug design

2017 ◽  
Vol 73 (3) ◽  
pp. 279-285
Author(s):  
Charlotte M. Deane ◽  
Ian D. Wall ◽  
Darren V. S. Green ◽  
Brian D. Marsden ◽  
Anthony R. Bradley
Keyword(s):  
Three Dimensional ◽  
Structural Data ◽  
Data Sets ◽  
Interaction Data ◽  
Activity Data ◽  
Ligand Interaction ◽  
Structure Based Drug Design ◽  
Web Based ◽  
Protein Ligand Interaction ◽  
Ligand Activity

In this work, two freely available web-based interactive computational tools that facilitate the analysis and interpretation of protein–ligand interaction data are described. Firstly,WONKA, which assists in uncovering interesting and unusual features (for example residue motions) within ensembles of protein–ligand structures and enables the facile sharing of observations between scientists. Secondly,OOMMPPAA, which incorporates protein–ligand activity data with protein–ligand structural data using three-dimensional matched molecular pairs.OOMMPPAAhighlights nuanced structure–activity relationships (SAR) and summarizes available protein–ligand activity data in the protein context. In this paper, the background that led to the development of both tools is described. Their implementation is outlined and their utility using in-house Structural Genomics Consortium (SGC) data sets and openly available data from the PDB and ChEMBL is described. Both tools are freely available to use and download at http://wonka.sgc.ox.ac.uk/WONKA/ and http://oommppaa.sgc.ox.ac.uk/OOMMPPAA/.

The unequal commute: Comparing commuting patterns across income and racial worker subgroups

2021 ◽  
pp. 0308518X2110688
Author(s):  
Yujie Hu
Keyword(s):  
Visual Analytics ◽  
Spatial Interaction ◽  
Interaction Model ◽  
Spatial Dimension ◽  
Harris County ◽  
Commuting Flows ◽  
Choropleth Mapping ◽  
Household Dynamics ◽  
Journey To Work ◽  
Commuting Patterns

The spatial dimension of the journey-to-work has important implications for land use and development policymaking and has been widely studied. One thrust of this research is concerned with the disaggregation of workers into subgroups for understanding disparities in commute. Most of these studies, however, were limited to the disaggregation by single socioeconomic class. Hence, this research aims to examine commuting disparities across commuter subgroups stratified by two socioeconomic variables—income and race—using a visual analytics approach. By applying the doubly constrained spatial interaction model to the 2014 Longitudinal Employer-Household Dynamics data, this research first synthesizes commuting flows for Downtown Houston workers across income-race subgroups at the tract level in Harris County, Texas, USA. It then uses bivariate choropleth mapping to visualize the spatial distributions of major Downtown Houston commuter neighborhoods by income-race classes, and significant commuting disparities are identified across income-race subgroups. The results highlight the importance of considering income and race simultaneously for commuting research. The visualization could help policymakers clearly identify the unequal commute across worker subgroups and inform policymaking.

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