Motorbikes in Hanoi cause congestion and have road safety and environmental impacts. A recent directive will progressively ban petrol (gasoline) motorbikes from inner urban districts by 2030. This paper describes statistical and spatial analyses of a travel survey of ~25,700 Hanoi residents to model the relationships between different socio-economic factors and support for such a ban. It applies a standard global binomial logistic regression which is extended to a spatial regression to quantify how these relationships vary spatially. A novel Generalized Additive Model (GAM) smooth framework was used to construct spatially varying coefficient (SVC) models. Critically, the approach undertakes model selection, and evaluates multiple models (~350,000) to determine which variables to include in spatial smooths. The best ranked model by AIC included spatial smooths for all but one of the predictor variables indicating the presence strong spatial dependencies in response to predictor relationships. The global model identified car ownership, car use and private property ownership as the strongest positive predictors of ban support, and distance from public transport as the dominant negative predictor. The SVC GAM provided a substantially better fit than the global model, capturing almost twice as much variation in the outcome. Three groups of response to predictor relationships were identified. The mapped coefficient surfaces indicate how the response to predictor relationships vary over space, highlight a transit rich inner core and where the relationship with ban support varied. These findings have direct policy implications and demonstrate the inferential value of using non-stationary approaches to examine such data.

Abstract. This paper describes a two stage approach for identifying neighbourhood areas that may be undergoing gentrification related changes. It summarises classic hedonic house price data over time (2014–2023) for each neighbourhood, and compares neighbourhood average price with those of local nearby areas. This enables neighbourhoods experiencing high relative increases in price to be identified as potentially gentrifying areas. Social media data for these areas were extracted and analysed using a large language model which scored each individual social media post by the degree to which their content indicated that the neighbourhood is experiencing change, potentially providing confirmatory evidence or not of gentrification. A number of areas of further work are identified.

Many cities are facing challenges caused by the increasing use of motorised transport and Hanoi, Vietnam, is no exception. The proliferation of petrol powered motorbikes has caused serious problems of congestion, pollution, and road safety. This paper reports on a new survey dataset that was created as part of the Urban Transport Modelling for Sustainable Well-Being in Hanoi (UTM-Hanoi) project. The survey of nearly 30,000 respondents gathers data on households’ demographics, perceptions, opinions and stated behaviours. The data are informative in their own right and have also been used to experiment with multi-scale spatial statistics, synthetic population generation and machine learning approaches to predicting an individual’s perceptions of potential government policies. The paper reports on the key findings from the survey and conducts a technical validation to contrast the outcomes to similar datasets that are available.

The dependence on motorbikes has contributed to traffic problems in Hanoi, Vietnam. Policymakers have considered a controversial ban on nonelectric 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, especially in the Global South, with implications for evidence-based policy making. This paper presents the results of some exploratory data analysis and a machine learning application using a travel survey recently conducted in Hanoi. It aims to understand how residents perceive a potential motorbike ban, their perceptions of different mobility modes, as well as their future plans for mobility if motorbikes are banned. This data-driven analysis of policy scenarios shows that awareness of the potential ban, distance to public transport, and individual transport modal choice determine the acceptability of the proposed motorbike ban and its likely success. It also shows that policymakers in Hanoi should also consider citizens' plans for future vehicle ownership, as the analysis results suggest that cars are likely to replace motorbikes if the ban is implemented.

This study describes an approach for augmenting urban residential preference and hedonic house price models by incorporating Status-Quality Trade Off theory (SQTO). SQTO seeks explain the dynamic of urban structure using a multipolar, in which the location and strength of poles is driven by notions of residential status and dwelling quality. This paper presents in outline an approach for identifying status poles and for quantifying their effect on land and residential property prices. The results show how the incorporation of SQTO results in an enhanced understanding of variations in land / property process with increased spatial nuance. A number of future research areas are identified related to the status pole weights and the development of status pole index.

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.

Author(s): Malleson, Nick; Nguyen Thi Thuy, Hang; Bui Quang, Thanh; Kieu, Minh; Hoang Huu, Phe; Comber, Alexis | Abstract: 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.

Author(s): Comber, Alexis; Malleson, Nick; Nguyen Thi Thuy, Hang; Bui Quang, Thanh; Kieu, Minh; Huu Phe, Hoang; Harris, Paul | Abstract: This paper describes the novel development and application of a multi-scale geographically weighted discriminant analysis (MSGWDA). This is applied to a case study of survey data of attitudes to a proposed motorbike / scooter ban in Han Noi, Vietnam. It uses discriminant analysis to examine attitudes to the ban in relation to travel purposes, distances, respondent age and so on. The main part of the paper focuses on describing the novel MSGWDA approach, and the results indicate the varying scales of relationship between the different input variables and the categorical responses variable. The paper also reflects on the pervasive logic of the approaches used to fit multiscale geographically weighted bandwidths (for example in regression). These have historically been based on the iterative back-fitting approaches used in GAMs, but risk missing potentially important variable interactions amongst un-evaluated bandwidths because of the sequence of their application. It is argued that although pragmatic in the 1990s, it may be possible to apply more deterministic approaches with increased memory and readily accessible computing power in order to better navigate such highly dimensional search spaces.

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.

This paper applies a local analysis to model and predict hedonic house price in Hanoi, Vietnam. It applies a locally compensated geographically weighted ridge regression to data survey data collected to support the Status Quality Trade Off theory proposed by Phe and Wakely (2000). This has an inherently local flavour is therefore suitable for local statistical approaches such as GWR (Brunsdon et al., 1996). The locally compensated ridge regression accounts for the observed local collinearity. The results provide a spatially nuanced model of status poles associated with areas of desirable housing. Some key areas for future work are suggested.

This paper applies a local analysis to model and predict hedonic house price in Hanoi, Vietnam.It applies a locally compensated geographically weighted ridge regression to data survey data collected to support the Status Quality Trade Off theory proposed by Phe and Wakely (2000).This has an inherently local flavour is therefore suitable for local statistical approaches such as GWR (Brunsdon et al., 1996).The locally compensated ridge regression accounts for the observed local collinearity.The results provide a spatially nuanced model of status poles associated with areas of desirable housing.Some key areas for future work are suggested.

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.

With the development of urbanization more focusing on quality and human-orientation in the transitional period, the mixed characteristics and challenges in peri-urban China are emerging. The emergence and promotion of Area Development PPP (ADP) as an effective and innovative development model in peri-urban areas offer a feasible way to address the development needs. This paper is trying to explore and examine this innovative model firstly by identifying the three main features of the mixed land development in peri-urban China in terms of properties, functions and the development patterns. Secondly, this paper tries to describe and review the practice and spatial pattern of PPP (Public Private Partnership) and ADP projects in China. Thirdly, this paper takes Gu'an Industrial Park PPP project as the typical case to further verify the effectiveness of the ADP model. Finally, this paper attempts to answer the question how to realize the mixed land use by discussing the rationale and key points of the ADP model in combination with the theory of urban growth coalitions. The research shows that the ADP model can be a successful solution for quality peri-urban development in terms of mixed land use. This model may lay the foundation for in-depth comparative study and provide useful references for other developing countries in their peri-urban development.