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The Modifiable Areal Unit Problem or MAUP is frequently alluded to but rarely addressed directly. The MAUP posits that statistical distributions, relationships and trends can exhibit very different properties when the same data are aggregated or combined over different reporting units or scales. This paper explores a number of approaches for determining appropriate scales of spatial aggregation. It examines a travel survey, undertaken in Ha Noi, Vietnam, that captures attitudes towards a potential ban of motorised transport in the city centre. The data are rich, capturing travel destinations, purposes, modes and frequencies, as well as respondent demographics (age, occupation, housing etc) including home locations. The dataset is highly dimensional, with a large n (26339 records) and a large m (142 fields). When the raw individual level data are used to analyse the factors associated with travel ban attitudes, the resultant models are weak and inconclusive - the data are too noisy. Aggregating the data can overcome this, but this raises the question of appropriate aggregation scales. This paper demonstrates how aggregation scales can be evaluated using a range of different metrics related to spatial and non-spatial variances. In so doing it demonstrates how the MAUP can be directly addressed in analyses of spatial data.

The dependence on motorbikes has contributed to severe traffic problems in Hanoi, Vietnam. Policymakers have considered a controversial ban on non-electric motorbikes in parts of the city in an effort to reduce congestion and pollution. However, understanding of individual perceptions on critical transport policies, such as this potential ban, is lacking. This paper applies a machine learning algorithm (XGBoost) to a bespoke travel survey to better understand how residents perceive a potential motorbike ban and how their perceptions might change under different policy scenarios. Our results suggest that prior awareness of the ban and shorter distances to public transport both increase peoples’ favour.

Hanoi is a rapidly expanding city with sprawling infrastructure dominated by private vehicles— predominantly motorbikes—that often follow an informal set of traffic rules. Therefore, a bespoke household survey is currently being conducted that collects information about demographics, trip data, vehicle ownership, attitudes towards a proposed motorbike ban and others. This project develops a data dashboard using R Shiny to serve as a communication tool of the survey findings. The interim development of the dashboard is discussed with the breakdown of both the functionality (what the user sees) and the code (the development process and the structure of the app).

In the city of Hanoi, Vietnam, as with other rapidly-developing cities, transport infrastructure is failing to keep pace with the burgeoning population. This has lead to high levels of congestion, air pollution, and a broad inequity in the accessibility of large parts of the city to residents. The emerging discipline of Urban Data Science has a valuable role in providing policy makers with robust evidence on which to base policy, but the discipline faces problems with the application of techniques that are based on assumptions that do not hold when applied to emerging economies.This paper presents the preliminary outputs of a new programme of urban data science work that is being developed specifically for Hanoi. It leverages a spatial microsimulation approach to up-sample a bespoke travel survey and create a synthetic representation of the transport preferences of all residents in the city. These new data are used to assess the impacts that changes in the broader socio-economic context, such as increasing prosperity amongst residents, could have on rates of car ownership and hence on the problems of congestion and pollution. The results begin to highlight parts of the city where the impacts of improved economic conditions coupled with changes to wider transport policies might lead to greater use of personal cars in the future.

This paper explores the impact of different distance metrics on collinearity in local regression models such as geographically weighted regression. Using a case study of house price data collected in Hà Nội, Vietnam, and by fully varying both power and rotation parameters to create different Minkowski distances, the analysis shows that local collinearity can be both negatively and positively affected by distance metric choice. The Minkowski distance that maximised collinearity in a geographically weighted regression was approximate to a Manhattan distance with (power = 0.70) with a rotation of 30°, and that which minimised collinearity was parameterised with power = 0.05 and a rotation of 70°. The results indicate that distance metric choice can provide a useful extra tuning component to address local collinearity issues in spatially varying coefficient modelling and that understanding the interaction of distance metric and collinearity can provide insight into the nature and structure of the data relationships. The discussion considers first, the exploration and selection of different distance metrics to minimise collinearity as an alternative to localised ridge regression, lasso and elastic net approaches. Second, it discusses the how distance metric choice could extend the methods that additionally optimise local model fit (lasso and elastic net) by selecting a distance metric that further helped minimise local collinearity. Third, it identifies the need to investigate the relationship between kernel bandwidth, distance metrics and collinearity as an area of further work.

GIScience 2016 Short Paper Proceedings Local variation in hedonic house price, Hanoi: a spatial analysis of SQTO theory A. J. Comber 1 , P. Harris 2 , T. Q. Nguyen 3 , P. K. Chi 4 , H. Tran 4 and H. H. Phe 5 School of Geography, University of Leeds, LS2 9JT, UK Email: a.comber@leeds.ac.uk Rothamsted Research, North Wyke, Okehampton, EX20 2SB, UK Email: paul.harris@rothamsted.ac.uk National University of Civil Engineering, Hanoi, Vietnam Email: quannt@nuce.edu.vn GeoViet Consulting Co. Ltd, Hanoi, Vietnam Email: chi.geoviet@gmail.com Vinaconex RD Fujita, 1989). In these, access to place of work to minimise commuting costs, for example, is one of the main factors explaining residential location choices for lower- income households and higher-income households choose larger properties in the suburbs

The advent of “ Doi Moi ” (renovation) in Vietnam has brought in a hitherto implicit residential property market. The dynamics of residential markets in Central Hanoi is a result of actions by groups trying to improve their properties following different investment strategies. A taxonomy of home improvers reveals three main types: the improver-consumerists, the improver-turned-dealers and the aspiring improvers. Their actions within the changing institutional framework is conceptualised in a dynamic model that could be a convenient tool for analysis of the residential property market.

The existing models of residential location are facing difficulties in explaining new trends in urban development such as gentrification and abandonment. The mainstream approach which stresses the bid-rent formulations and the access/space trade-off seems to be at variance with the current reality of dispersal of both industry and housing in modern cities. In this paper, it is proposed that the focus on the city centre(s) and distance(s) from it (or them) should be shifted to two other categories of parameter: housing status and dwelling quality. A model of interaction between these parameters can be used not only to describe but also to predict various types of residential development in different urban contexts. The components of a new theory of residential location are proposed.

Housing markets are at the centre of the recent global financial turmoil. In this well-researched study, a multidisciplinary group of leading analysts explores the impact of the crisis within, and between, countries.