Multi-criteria suitability analysis and spatial interaction modeling of retail store locations in Ontario, Canada

GIS-based decision analysis is increasingly used by retailers to address the complexity and cost of investment in retail store location decisions. This study conceptualizes and represents nine criteria in a GIS-based multi-criteria decision analysis of 4.7 million potential retail store locations. From topographic statistics to spatial interaction modelling, the study utilizes criteria of varied complexity to analyze the statistical and spatial distribution of highly suitable locations for a retail store. The study further examines how the spatial representations of criteria based on the Huff model affects the distribution of suitable locations. The results show that although Toronto dominates the retail landscape in Ontario, key regions are found in Guelph, Kitchener-Waterloo and Cambridge. Results show that the incorporation of network-based spatial interaction costs in Huff’s model produces more spatially heterogeneous sales estimates than Euclidean-based spatial interactions. Future research efforts in improving various components of the suitability analysis, as well as the scaling and regional parameterization of spatial interaction models are also discussed

Retail property market performance of cities: an investigation of the relationships between spatial configuration of consumer movement and changes in retail stock and value in Leeds, Newcastle and York

The influence of consumer activities on the performance of retail locations and retail property market in cities can be critical. This is because where and how retail consumers choose to transact influences the locational performance of retail property markets in cities. This study investigates relationships between consumer movement and the performance of retail property markets (RPM) between 2010 and 2017 in York, Leeds and Newcastle. The study adopts the spatial configuration (street segment) analysis technique to compute consumer movement patterns (CMP) on the sampled cities’ layouts using DepthMapX to obtain the CMP variables; specifically, integration, choice and NACH metrics. The RPM data were sourced from valuation summary lists belonging to the VOA dataset and analysed using MS Access and MS Excel to obtain RPM variables, namely, changes in retail rental value and changes in retail stock across locations. The study investigates the spatial and statistical relationships between the CMP and RPM variables of cities at mesoscales and macroscales using QGIS and SPSS tools, respectively. The spatial investigations visualise locational relationships between changes in RPM variables and the spatial accessibility index of the CMP variables. The statistical analyses adopted Spearman-rho coefficients to investigate the rank correlation between the RPM and CMP variables. Further statistical (multiple regression) analysis were undertaken to estimate the locational performance of the RPM (dependent variable) using the CMP (independent variables) across all the estimable city layouts. Findings show that there are significant relationships between changes in retail rental value and all the CMP variables at York mesoscale, Leeds mesoscale and Newcastle macroscale. The results indicate that the relationship between configured consumer movement and changes in retail rental value are influenced by scale and city characteristics. The research is the first to estimate the location performance of commercial property by way of spatial configuration analysis. The research outputs are useful tools for retail property market actors to make locational decisions on investments, occupation, development and the strategic management of urban retail space. The study recommends further studies on the prospects of spatial configuration analysis and other methods in estimating the future performance of the commercial property market for optimum utilisation and the management of urban resources

Enhanced Huff Model for Estimating Park and Ride (PnR) Catchment Areas in Perth, WA

A train station catchment area delineates the spatial territory from which the users of a train station are drawn. The size and shape of this catchment can be influenced by a variety of factors, such as the transport network, the location of stations and the service quality they offer, as well as the land use density and diversity in the transport corridor. Although numerous studies have been conducted to understand the size of catchment areas, limited research has focused on determining the spatial boundary (shape) of train station catchments. This paper develops a framework for deriving a spatial boundary of a Park and Ride (PnR) catchment area by incorporating the Huff model and Geographic Information Systems (GIS) technologies. The approach is staged, firstly determining the PnR station choice as a function of the attractiveness of a train station and the cost of access between the origin (such as a suburb) and the destination of a trip (such as the Perth CBD). Linear referencing method is then applied to re-define the origins to train stations based on the derived station choice probability. Finally, the spatial boundary of a catchment area is determined according to the adjusted origins, using GIS technologies. The model outputs were evaluated against licence plate survey of station users, where the Kappa coefficient (0.74) and overall accuracy (0.88) statistic suggested that the model's results are robust. The paper then shows how catchment area data can be used to better manage travel demand and plan design solutions aimed at increased accessibility to train stations

Spatial Interaction Models in a Big Data Grocery Retailing Environment

Grocery expenditure is responsible for around 10% of total household spend in the UK, making the grocery retail market worth over £200bn a year in 2021. The size of this market and the nature of retailing competition makes it important for retailers to make the right decisions. One such decision is the location of their stores for which there have been a number of changes in the location, format and channel of consumer interaction along with the methods that have been employed to determine new store location. In recent years it has been suggested that the spatial interaction model is the most appropriate method for estimating new store revenue and hence location. However, previous attempts to explore the performance of the spatial interaction model in grocery retailing have been limited by access to loyalty card data. In this thesis we show that these models are unable to account for the heterogeneity in store conditions and consumer behaviour to model total store revenue. Notably, we find that at the regional scale the size of the errors are such that these models are unlikely to be used consistently in practice for estimating store revenue or locating new stores. Furthermore, that the performance achieved in previous applications are unlikely to be consistently replicated. Thus our results demonstrate that the spatial interaction model in its current form is no longer appropriate for modelling grocery store revenue. It is anticipated that these results may become a starting point for the development and application of alternative forms of models and methods for predicting grocery retailing store revenue. Notably, such new methods must be able to account for recent changes in consumer behaviour such as convenience store shopping, multi-purpose trips and the growing influence of e-commerce, alongside changes in retailers interaction strategies

Urban network analysis training in Rhinoceros3D : Barcelona, 11-13 July 2022

"ETSAB, DUOT, LUB" -- Portada"Course instructor: Andres Sevtsuk (associate professor of urban science and planning at MIT, Director of the City Form Lab)" -- Coberta"ETSAB summer masterclass" -- CobertaDescripció del recurs: 20 setembre 2023Conté: Foreword: Walking the streets: an approach to urban proximity through the analysis of pedestrian networks / Eulàlia Gómez-Escoda -- Presentation: Urban Network Analysis tools for modeling land use and transportation interactions for pedestrians and cyclists / Andres Sevtsuk -- Barcelona’s Superblocks under the spotlight: evaluating expected impact of green axes in pedestrian route choice and retail footfall / Mikel Berra-Sandín and Enric Villavieja Martínez -- The walking routes of the children of La Vila de Gràcia / André Salazar and Ricardo Sotomayor -- Walking the hills. Analysis of the walkable network around existing and planned L9 metro stations in upper Gràcia, Barcelona / Álvaro Clua, Francesc Valls and Joan Martí Elia

Targeted Investment for Food Access

This project focuses on modeling access to food locations by identifying the most critical roadway links in a transportation network. This project extends the Critical Closeness Accessibility (CCA) measure developed by Novak and Sullivan (2014) to identify the roadway infrastructure components that are most critical with respect to food accessibility. Specifically, origin and destination weighting are included for the application of food security, where origins are weighted according to household vulnerability and destinations are weighted by retail-grocery square footage. The CCA is further extended by calibrating the trip impedance constant, ω, in the original formulation of the CCA with actual grocery-shopping data from the National Household Travel Survey. This calibration modifies the functional form of the accessibility measure to address trips focused on food access and thus incorporates realistic travel expectations for retail grocery familiarity of households. The project also provides a unique method for estimating household level vulnerability characteristics using population synthesis. The modification of the CCA to address food accessibility can be used to support more targeted investment in transportation assets, as the CCA is indexed to specific roadway links in the network. The methodology is demonstrated using the Travel Demand Model of Chittenden County, Vermont

Pedestrian routes and accessibility to urban services: an urban rhythmic analysis on people's behaviour before and during the COVID-19.

The emergency of COVID-19 changed the face of our cities, preventing most of the urban activities, limiting travels on large, medium and short distances and drastically reducing the number and the intensity of social relationships. The restrictive measures, imposed to the entire population, sensibly affected the experience of our built environment as well as the assets of pedestrian and cycling network that lead to the achievement of essential urban services. On one hand these limitations drastically imposed a change in the people's habits who spend now more time walking and cycling in absence of any other entertainments; on the other, they have revealed the need of a reorganisation of pedestrians and cycling paths as well as of open spaces. The morphology of these urban spaces are unable to cope with the current social-distancing situation and to adapt to a “new different routine”. Local decision makers face with a new demand of urban space for pedestrian and cycling accessibility which have been so far unexplored. In order to contribute to future planning decisions, the document proposes a comparison between pedestrian flows and accessibility to urban services during the blockade, taking two districts in the city of Aberdeen as a case study. By adopting an urban rhythmic analysis, the selected areas were monitored on a weekly basis during different periods during the days in order to quantify the intensity of the user, the available services and their opening and closing times also change the date obtained from rhythmic analysis they are associated in a GIS environment in order to classify urban areas. Drawn on the concepts of new social distancing and switch of life/working habits as main factors for redesigning the pedestrian and cycling urban spaces, the paper proposes, as a conclusion, specific urban design recommendations in order to deal with emergency situations, such as an outbreak movement limitation

Using network analysis to explore the effects of road network on traffic congestion and retail store sales

The physical road system plays a critical role in environmental and city planning. In the context of retail store site-selection, measures of accessibility and the ease and willingness of consumers to shop at a store can be essential to revenue generation and retail success. To quantify accessibility requires a detailed examination of the road networks and in many cases modelling to estimate potential traffic congestion that would inhibit accessibility. The application of network theory to assess the accessibility of road segments and land parcels is non-existent. Research on the effects of the structure of the road network, via network analysis, can facilitate identifying potential congestion issues and subsequently the effects of congestion on commercial performance (e.g., retail sales). The application of network analysis to a road network is distinctive from applications in other disciplines (e.g., sociology, ecology), since, among other network attributes, the road network is a low-dimension, link-centroid, and relatively static system with time-variant traffic flow. In addition to conceptually interrogating the difference between social and road networks for network analysis, the presented research results show the relationship among different network metrics and simulated traffic congestion and the strength of the relationship between network metrics and retail sales relative to socio-demographic and site-location characteristics