A primer for working with the Spatial Interaction modeling (SpInt) module in the python spatial analysis library (PySAL)

Oshan, Taylor M.

doi:10.18335/region.v3i2.175

Cited by 27 publications

(23 citation statements)

References 23 publications

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“…where ln is the natural logarithm, and μ i and α j are origin-based and destination-based binary indicator variables that ensure the constraints enforced by the multiplicative balancing factors are met. The computation of the model was carried out using the spint module of the Python spatial analysis library (PySAL) and further details regarding model formulation, calibration, and interpretation are available in (Oshan, 2016).…”

Section: Analyzing the Relationship Between Urban Mobility And Segregationmentioning

confidence: 99%

“…As the results are based on a particular framework with a limited set of variables, a more detailed application of the result requires further investigation of diverse and complex socioeconomic and geographic factors. Variations within a study area could be investigated by decomposing a flow dataset by origin and individually analyzing trips from each origin to all other destinations (Fotheringham, 1981;Nakaya, 2001;Oshan, 2016). It would then be possible to reveal whether or not distance-decay varies spatially and if any potential variation is correlated with other factors, such as residential segregation, income, education, or heath disparities.…”

Section: Weak Segregation In Mobility: Bubbles Can Be Brokenmentioning

confidence: 99%

See 1 more Smart Citation

Are we breaking bubbles as we move? Using a large sample to explore the relationship between urban mobility and segregation

Park

Oshan

Ali

et al. 2021

Computers, Environment and Urban Systems

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Section: Analyzing the Relationship Between Urban Mobility And Segregationmentioning

confidence: 99%

Section: Weak Segregation In Mobility: Bubbles Can Be Brokenmentioning

confidence: 99%

Are we breaking bubbles as we move? Using a large sample to explore the relationship between urban mobility and segregation

Park

Oshan

Ali

et al. 2021

Computers, Environment and Urban Systems

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“…The guide includes all of the data and code you need to run a spatial interaction model, and can be accessed via the link at the beginning of this paper 1 . If, however, you would like to explore these models in Python, then Oshan (2016) has written an excellent primer that is worth reading, while Dan Lewis has translated a similar R walk-through of mine into Python using UK data 2 . For consistency, Oshun's notation is adopted in this paper.…”

Section: Modelling Population Flows In Practicementioning

confidence: 99%

“…It will just come down to which fits the data better. Following Oshan's (2016) example, the accompanying walk-through exercise will allow you to explore the effect on model fits and predictions of fitting different distance decay functions to the distance variable.…”

Section: Further Experimentationmentioning

confidence: 99%

Modelling population flows using spatial interaction models

Dennett¹

2018

Aust Popul Stud

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Background Spatial Interaction Models have been used for decades to explain and predict flows (of migrants, capital, traffic, trade etc.) between geographic locations.Aims This paper will guide users through the process of fitting and calibrating spatial interaction models in order to understand, explain and predict internal migration flows in Australia. Data and methods Internal migration data from the Australian 2011 Census of Population and Housing, which records people who have moved between Greater Capital City Statistical Areas over 5-year periods, is used to exemplify the modelling process. The R statistical software is used to process and visualise the data as well as run the models. Results The full suite of Wilson’s family of spatial interaction models is fitted to the internal migration data, revealing that distance and origin/destination populations are some of the most important influencing factors affecting internal migration flows. We see whether constraining the model to known flows about origins and/or destinations will improve the fits and model estimates. Conclusions Spatial interaction modelling has been a tool in the box of some population geographers for a number of decades. However, recent advances in more forgiving programming languages such as R and Python now mean that this powerful modelling methodology is no longer only available to those who also possess advanced computer programming skills. This guide has exemplified the process of fitting and calibrating spatial interaction models on Australian internal migration data, but the methods could easily be applied to other flow data sets in other contexts.

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“…5 as a log-linear model (see Oshan 2016) and using a negative binomial regression model fitted using the MASS package in R to estimate flows and parameters. By aggregating estimated flows and residuals to land-use-type clusters for origins and destinations, it is possible to see where call volumes are higher or lower than expected between and within clusters across the city of Dakar (Table 2).…”

Section: Model Of Call Interactionmentioning

confidence: 99%

New Forms of Data for Understanding Urban Activity in Developing Countries

Manley

Dennett

2018

Appl. Spatial Analysis

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Emerging forms of data offer new opportunities for developing a deeper understanding of poorly understood social and spatial processes. This is no more important than in developing countries, where large-scale data collection and processing has been relatively limited. In this paper, we explore how two new datasets can be used to enhance our understanding of human activity and communication interactions in Dakar, Senegal. Starting from a premise of little contextual knowledge about the setting in which we are working, we explore how much these data, combined with novel quantitative methods, are able to inform us about the urban environment in question. Fine-grained infrastructural data are combined with k-means clustering to produce an 11-class land use classification, distinguishing dense and sparse, single and mixed use regions. Using these classifications and over 1.5 billion mobile phone call records, patterns of activity and interaction within and between land use types are analysed. These analyses reveal strong activity associated with high density commercial, governmental and administrative regions. These regions are also identified as relatively strong 'attractors' of communication, and wider patterns show higher interactions between areas with similar land use characteristics. Analyses of dynamic activity and interaction patterns highlight the movement of people from workplace zones to residential areas. Importantly, these analyses reflect the expected patterns of urban activity, providing some validation of the data, methods, and the empirical approach. The paper concludes in addressing the strengths and potential of these approaches, while recognising current limitations and areas for further work.

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A primer for working with the Spatial Interaction modeling (SpInt) module in the python spatial analysis library (PySAL)

Cited by 27 publications

References 23 publications

Are we breaking bubbles as we move? Using a large sample to explore the relationship between urban mobility and segregation

Are we breaking bubbles as we move? Using a large sample to explore the relationship between urban mobility and segregation

Modelling population flows using spatial interaction models

New Forms of Data for Understanding Urban Activity in Developing Countries

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