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This work seeks to introduce improvements to the traditional variable selection procedures employed in the development of geodemographic classifications. It presents a proposal for shifting from a traditional approach for generating general-purpose one-size-fits-all geodemographic classifications to application-specific classifications. This proposal addresses the recent scepticism towards the utility of general-purpose applications by employing supervised machine learning techniques in order to identify contextually relevant input variables from which to develop geodemographic classifications with increased discriminatory power. A framework introducing such techniques in the variable selection phase of geodemographic classification development is presented via a practical use-case that is focused on generating a geodemographic classification with an increased capacity for discriminating the propensity for Library use in the UK city of Leeds. Two local classifications are generated for the city, one a general-purpose classification, and the other, an application-specific classification incorporating supervised Feature Selection methods in the selection of input variables. The discriminatory power of each classification is evaluated and compared, with the result successfully demonstrating the capacity for the application-specific approach to generate a more contextually relevant result, and thus underpins increasingly targeted public policy decision making, particularly in the context of urban planning.
Amanda Otley; Michelle Morris; Andy Newing; Mark Birkin. Local and Application-Specific Geodemographics for Data-Led Urban Decision Making. Sustainability 2021, 13, 4873 .
AMA StyleAmanda Otley, Michelle Morris, Andy Newing, Mark Birkin. Local and Application-Specific Geodemographics for Data-Led Urban Decision Making. Sustainability. 2021; 13 (9):4873.
Chicago/Turabian StyleAmanda Otley; Michelle Morris; Andy Newing; Mark Birkin. 2021. "Local and Application-Specific Geodemographics for Data-Led Urban Decision Making." Sustainability 13, no. 9: 4873.
In this paper, we detail an individual-level analysis of under-exploited location-based social network (LBSN) data extracted from Sina Weibo, a comprehensive source for data-driven research focused on Chinese populations. The richness of the Sina Weibo data, coupled with high-quality venue and attraction information from Foursquare, enables us to track Chinese tourists visiting London and understand behaviours and mobility patterns revealed by their activities and venue-based ‘check-ins’. We use these check-ins to derive a series of indicators of mobility which reveal aggregate and individual-level behaviours associated with Chinese tourists in London, and which act as a tool to segment tourists based on those behaviours. Our data-driven tourist segmentation indicates that different groups of Chinese tourists have distinctive activity preferences and travel patterns. Our primary interest is in tourists’ consumption behaviours, and we reveal that tourists with similar activity preferences still exhibit individualised behaviours with regards to the nature and location of key consumption activities such as shopping and dining out. We aim to understand more about Chinese tourist shopping behaviours as a secondary activity associated with multi-purpose trips, demonstrating that these data could permit insights into tourist behaviours and mobility patterns which are not well captured by official tourism statistics, especially at a localised level. This analysis could be up-scaled to incorporate additional LBSN data sources and broader population subgroups in order to support data-driven urban analytics related to tourist mobilities and consumption behaviours.
Zi Ye; Andy Newing; Graham Clarke. Understanding Chinese tourist mobility and consumption-related behaviours in London using Sina Weibo check-ins. Environment and Planning B: Urban Analytics and City Science 2020, 1 .
AMA StyleZi Ye, Andy Newing, Graham Clarke. Understanding Chinese tourist mobility and consumption-related behaviours in London using Sina Weibo check-ins. Environment and Planning B: Urban Analytics and City Science. 2020; ():1.
Chicago/Turabian StyleZi Ye; Andy Newing; Graham Clarke. 2020. "Understanding Chinese tourist mobility and consumption-related behaviours in London using Sina Weibo check-ins." Environment and Planning B: Urban Analytics and City Science , no. : 1.
Purpose The purpose of this paper is to demonstrate that applied spatial modelling can inform the planning, delivery and evaluation of retail services, offering improvements over traditional retail impact assessment (RIA), especially within localities which experience seasonal fluctuations in demand. Design/methodology/approach The paper first describes a new theoretically informed tourist-based spatial interaction model (SIM) which has been custom-built and calibrated to capture the dynamics of the grocery sector in Cornwall, UK. It tests the power of the model to predict store performance for stores not used in the original calibration process, using client data for a new store development. The model is operationalised for the evaluation of various retail development schemes, demonstrating its contribution across a full suite of location decision making application areas. Findings The paper demonstrates that this highly disaggregate modelling framework can provide considerable insight into the local economic and social impacts of new store developments, rarely addressed in the retail location modelling literature. Practical implications Whilst SIMs have been widely used in retail location research by the private sector, the paper shows that such a model can have considerable value for public sector retail planning, a sector which seemed to have abandoned such models from the 1980s onwards, replacing them with often very limited and crude RIA. Originality/value The ability to review the forecasting capabilities of a model (termed post-investment review) are very rare in academic research. This paper offers new evidence that SIMs can support the RIA process.
Andy Newing; Graham Clarke; Martin Clarke. Applied spatial modelling for retail planning in tourist resorts. International Journal of Retail & Distribution Management 2018, 46, 1117 -1132.
AMA StyleAndy Newing, Graham Clarke, Martin Clarke. Applied spatial modelling for retail planning in tourist resorts. International Journal of Retail & Distribution Management. 2018; 46 (11/12):1117-1132.
Chicago/Turabian StyleAndy Newing; Graham Clarke; Martin Clarke. 2018. "Applied spatial modelling for retail planning in tourist resorts." International Journal of Retail & Distribution Management 46, no. 11/12: 1117-1132.
Evolving consumer behaviours with regards to store and channel choice, shopping frequency, shopping mission and spending heighten the need for robust spatial modelling tools for use within retail analytics. In this paper, we report on collaboration with a major UK grocery retailer to assess the feasibility of modelling consumer store choice behaviours at the level of the individual consumer. We benefit from very rare access to our collaborating retailers’ customer data which we use to develop a proof-of-concept agent-based model (ABM). Utilising our collaborating retailers’ loyalty card database, we extract key consumer behaviours in relation to shopping frequency, mission, store choice and spending. We build these observed behaviours into our ABM, based on a simplified urban environment, calibrated and validated against observed consumer data. Our ABM is able to capture key spatiotemporal drivers of consumer store choice behaviour at the individual level. Our findings could afford new opportunities for spatial modelling within the retail sector, enabling the complexity of consumer behaviours to be captured and simulated within a novel modelling framework. We reflect on further model development required for use in a commercial context for location-based decision-making.
Charlotte Sturley; Andy Newing; Alison Heppenstall. Evaluating the potential of agent-based modelling to capture consumer grocery retail store choice behaviours. The International Review of Retail, Distribution and Consumer Research 2017, 28, 27 -46.
AMA StyleCharlotte Sturley, Andy Newing, Alison Heppenstall. Evaluating the potential of agent-based modelling to capture consumer grocery retail store choice behaviours. The International Review of Retail, Distribution and Consumer Research. 2017; 28 (1):27-46.
Chicago/Turabian StyleCharlotte Sturley; Andy Newing; Alison Heppenstall. 2017. "Evaluating the potential of agent-based modelling to capture consumer grocery retail store choice behaviours." The International Review of Retail, Distribution and Consumer Research 28, no. 1: 27-46.
This paper assesses the feasibility of determining key household characteristics based on temporal load profiles of household electricity demand. It is known that household characteristics, behaviours and routines drive a number of features of household electricity loads in ways which are currently not fully understood. The roll out of domestic smart meters in the UK and elsewhere could enable better understanding through the collection of high temporal resolution electricity monitoring data at the household level. Such data affords tremendous potential to invert the established relationship between household characteristics and temporal load profiles. Rather than use household characteristics as a predictor of loads, observed electricity load profiles, or indicators based on them, could instead be used to impute household characteristics. These micro level imputed characteristics could then be aggregated at the small area level to produce ‘census-like’ small area indicators. This work briefly reviews the nature of current and future census taking in the UK before outlining the household characteristics that are to be found in the UK census and which are also known to influence electricity load profiles. It then presents descriptive analysis of two smart meter-like datasets of half-hourly domestic electricity consumption before reporting the results of a multilevel model-based analysis of the same data. The work concludes that a number of household characteristics of the kind to be found in UK census-derived small area statistics may be predicted from particular load profile indicators. A discussion of the steps required to test and validate this approach and the wider implications for census taking is also provided.
Ben Anderson; Sharon Lin; Andy Newing; Abubakr Bahaj; Patrick James. Electricity consumption and household characteristics: Implications for census-taking in a smart metered future. Computers, Environment and Urban Systems 2017, 63, 58 -67.
AMA StyleBen Anderson, Sharon Lin, Andy Newing, Abubakr Bahaj, Patrick James. Electricity consumption and household characteristics: Implications for census-taking in a smart metered future. Computers, Environment and Urban Systems. 2017; 63 ():58-67.
Chicago/Turabian StyleBen Anderson; Sharon Lin; Andy Newing; Abubakr Bahaj; Patrick James. 2017. "Electricity consumption and household characteristics: Implications for census-taking in a smart metered future." Computers, Environment and Urban Systems 63, no. : 58-67.
We explore the value of recently released workplace geographies and accompanying census-based workplace zone statistics (WZS) and an associated classification of workplace zones (COWZ). We consider how these data could support retailers in their operational and strategic decision-making, including the evaluation of retail demand and retail store performance in localities where trade is driven by non-residential demand. In collaboration with major UK grocery retailer The Co-operative Group we explore the relationship between workplace population composition and store trading characteristics using a series of case study stores within Inner London. We use empirical store trading data to identify store and product category level temporal sales fluctuations attributable to workplace populations. We also use census-derived flow data to identify the spatial origins of workplace population inflow. We identify that store performance exhibits characteristics attributable to demand driven by these populations. We conclude that workplace population geographies, WZS and the COWZ afford considerable potential for understanding drivers of store performance, observed store trading patterns and evaluation of retail store performance. We suggest that the next step is to build these populations and their micro geography spatial and temporal characteristics into predictive models and evaluate their potential for store performance evaluation and location-based store and network decision-making within this sector.
Tom Berry; Andy Newing; Deborah Davies; Kirsty Branch. Using workplace population statistics to understand retail store performance. The International Review of Retail, Distribution and Consumer Research 2016, 26, 1 -21.
AMA StyleTom Berry, Andy Newing, Deborah Davies, Kirsty Branch. Using workplace population statistics to understand retail store performance. The International Review of Retail, Distribution and Consumer Research. 2016; 26 (4):1-21.
Chicago/Turabian StyleTom Berry; Andy Newing; Deborah Davies; Kirsty Branch. 2016. "Using workplace population statistics to understand retail store performance." The International Review of Retail, Distribution and Consumer Research 26, no. 4: 1-21.
Human populations are not static or uniformly distributed across space and time. This consideration has a notable impact on natural hazard analyses which seek to determine population exposure and risk. This paper focuses on the coupling of population and environmental models to address the effect of seasonally varying populations on exposure to flood risk. A spatiotemporal population modelling tool, SurfaceBuilder247, has been combined with LISFLOOD-FP flood inundation model outputs for a study area centred on the coastal resort town of St Austell, Cornwall, United Kingdom (UK). Results indicate strong seasonal cycles in populations and their exposure to flood hazard which are not accounted for in traditional population datasets and flood hazard assessments. Therefore, this paper identifies and demonstrates considerable enhancements to the current handling of spatiotemporal population variation within hazard exposure assessment and disaster risk management.
Alan Smith; Andy Newing; Niall Quinn; David Martín; Samantha Cockings; Jeffrey Neal. Assessing the Impact of Seasonal Population Fluctuation on Regional Flood Risk Management. ISPRS International Journal of Geo-Information 2015, 4, 1118 -1141.
AMA StyleAlan Smith, Andy Newing, Niall Quinn, David Martín, Samantha Cockings, Jeffrey Neal. Assessing the Impact of Seasonal Population Fluctuation on Regional Flood Risk Management. ISPRS International Journal of Geo-Information. 2015; 4 (3):1118-1141.
Chicago/Turabian StyleAlan Smith; Andy Newing; Niall Quinn; David Martín; Samantha Cockings; Jeffrey Neal. 2015. "Assessing the Impact of Seasonal Population Fluctuation on Regional Flood Risk Management." ISPRS International Journal of Geo-Information 4, no. 3: 1118-1141.
The spatial interaction model (SIM) is an important tool for retail location analysis and store revenue estimation, particularly within the grocery sector. However, there are few examples of SIM development within the literature that capture the complexities of consumer behavior or discuss model developments and extensions necessary to produce models which can predict store revenues to a high degree of accuracy. This article reports a new disaggregated model with more sophisticated demand terms which reflect different types of retail consumer (by income or social class), with different shopping behaviors in terms of brand choice. We also incorporate seasonal fluctuations in demand driven by tourism, a major source of non‐residential demand, allowing us to calibrate revenue predictions against seasonal sales fluctuations experienced at individual stores. We demonstrate that such disaggregated models need empirical data for calibration purposes, without which model extensions are likely to remain theoretical only. Using data provided by a major grocery retailer, we demonstrate that statistically, spatially, and in terms of revenue estimation, models can be shown to produce extremely good forecasts and predictions concerning store patronage and store revenues, including much more realistic behavior regarding store selection. We also show that it is possible to add a tourist demand layer, which can make considerable forecasting improvements relative to models built only with residential demand.
Andy Newing; Graham P. Clarke; Martin Clarke. Developing and Applying a Disaggregated Retail Location Model with Extended Retail Demand Estimations. Geographical Analysis 2014, 47, 219 -239.
AMA StyleAndy Newing, Graham P. Clarke, Martin Clarke. Developing and Applying a Disaggregated Retail Location Model with Extended Retail Demand Estimations. Geographical Analysis. 2014; 47 (3):219-239.
Chicago/Turabian StyleAndy Newing; Graham P. Clarke; Martin Clarke. 2014. "Developing and Applying a Disaggregated Retail Location Model with Extended Retail Demand Estimations." Geographical Analysis 47, no. 3: 219-239.
National consumption-based emissions of households are typically disaggregated using consumption and expenditure microdata. This data collection contains neighbourhood- and product-level per capita household emissions for the year 2016 estimated using an input-output methodology and three such consumption and expenditure datasets for subnational disaggregation. These datasets include the Output Area Classification (a publicly available geodemographic classification), the Living Costs and Food Survey (an openly available expenditure survey), and a commercial household expenditure dataset by TransUnion. Data are available for Lower and Middle Layer Super Output Areas in England and Wales, to Data Zones and Intermediate Geographies in Scotland and to Super Output Areas and Wards in Northern Ireland. Product-level data are available at Classification of Individual Consumption by Purpose (COICOP) 2 and 3 levels.
Lena Kilian; Anne Owen; Andy Newing; Diana Ivanova. Per Capita Consumption-Based Greenhouse Gas Emissions for UK Lower and Middle Layer Super Output Areas, 2016. 2021, 1 .
AMA StyleLena Kilian, Anne Owen, Andy Newing, Diana Ivanova. Per Capita Consumption-Based Greenhouse Gas Emissions for UK Lower and Middle Layer Super Output Areas, 2016. . 2021; ():1.
Chicago/Turabian StyleLena Kilian; Anne Owen; Andy Newing; Diana Ivanova. 2021. "Per Capita Consumption-Based Greenhouse Gas Emissions for UK Lower and Middle Layer Super Output Areas, 2016." , no. : 1.