We next use three pieces of narratives—neighbourhood deprivation, internet usage, and residential movements and ethnicity—as case studies to demonstrate how web mapping can be used for interactive storytelling. Although only static screenshots of the maps are shown here, readers are encouraged to explore the full set of interactive maps and stories with embedded interactive maps (e.g. https://data.cdrc.ac.uk/stories/iuc). Relevant maps have one or more “story” web links shown on the side panel of the map. These link to long-form text articles on a complimentary website which may contain screenshots or embedded versions of the maps at relevant points in the text, to lead a reader through a step-by-step narrative related to the maps’ data.
Neighbourhood Deprivation and Inequalities
Inequalities, examined along different dimensions such as income, gender, ethnicity, health, and age, are the most debated topics of social investigations in the UK, which lead to unequal life chances and outcomes. For example, the UK Office for National Statistics reveals the uneven distribution of household wealth—the top decile of households owned 45% of total aggregate household wealth in the UK (Office for National Statistics 2015). Among many contributing factors, there are marked regional disparities and geographies of socio-economic inequalities in the UK observed as “coldspots” or “left-behind” areas (Local Trust & Oxford Consultants for Social Inclusion 2019), as well as the enduring “north–south divide” (Longley et al. 2021). The Index of Multiple Deprivation (MHCLG 2019) provides a summary measure of relative deprivation for the Lower Layer Super Output Area (LSOA, a UK Census release geography with an approximately 1500 population in England and Wales) in England across a spectrum of 7 weighted Domains: Income, Employment, Health Deprivation and Disability, Education, Crime, Barriers to Housing and Services, and Living Environment. Since geography plays a crucial part in the neighbourhood deprivation and inequalities in the UK, it is intuitive to represent the regional and local disparities by using web-based online maps. The CDRC Mapmaker has two maps related to this theme: Deprivation Indices (https://mapmaker.cdrc.ac.uk/#/index-of-multiple-deprivation) and Deprivation Rank Change (https://mapmaker.cdrc.ac.uk/#/deprivation-change).
Changes in deprivation rank over time are interesting indicators to explore, although they should never be compared on absolute scales. Moreover, with caveats such as possible calculation methodology and small area boundary changes, we take the Deprivation Rank Change map as a case study here to investigate the relative performance and changes of areas. We pan the map and zoom into a coastal town, Margate, on England’s southeast coast to illustrate the neighbourhood changes from 2015 to 2019 (see Fig. 2). Margate was historically a very popular seaside town but now is one of the most deprived places in the UK, although it still attracts a lot of holidaymakers from London. Figure 2 shows the rank changes of Cliftonville in the Thanet District of east Margate. Compared to traditional choropleth maps, our map foregrounds the building footprints and street networks and downplays the unpopulated areas such as the parks, farmlands, and beaches in dark grey on the map.
We also spot a stark contrast in relative deprivation rank changes 2015–2019 between the west (in blue) and east (in red) sides of Princess Margate Avenue. The eastern part of Cliftonville is reported to have about a 17% deprivation rank increase, which suggests this part has become more deprived relative to other areas in England during the four years of development; while on the other side of the street, the story is completely different—the western part relatively outperforms its counterparts by a 13% decrease in the rank. To track down the factors that affect the overall deprivation scores, further investigations can be achieved by using the dropdown menu on the right legend panel and visualising the indices from the 7 Domains separately. In so doing, we find that there is no significant change in the Environment Domain in both parts and that they both become more deprived in the Housing Domain and performed relatively well in the Crime Domain. However, the Income, Employment, Education and Health Domains make the two neighbouring areas end up with completely different performances with respect to the relative change in the overall deprivation rank. Therefore, when making localised revitalisation and levelling-up policies for these “left-behind” areas, considerations of improving the underperformed Domains should be prioritised.
Further investigations of neighbourhood changes can be conducted in combination with two other Mapmaker themes: the annual updated Ethnicity Estimator modelled from the Linked Consumer Registers (Lansley et al. 2019) and the Temporal Output Area Classification (TOAC). Besides, an “Urban/Buildings” toggle button is enabled in the layer control panel on the bottom left corner for users to switch between a standard choropleth map and a street/building based map. The underpinning data for making the map can be downloaded via the hyperlink in the right-side panel to the CDRC data service. In addition to that, the map can also be exported conveniently to a PDF report using the hyperlink in the panel.
Internet Usage and Digital Exclusion
Various emerging information and communications technologies have profoundly changed society and people’s lifestyles during the past decades, as netizens move part of their daily activities into the digital world and cyberspace—for example, online shopping, remote learning, video conferencing, online social network and so forth. The Covid-19 pandemic and lockdowns further thrust this trend due to different social distancing measures. However, it should never be overlooked that there are still many left-behind or even excluded members of the society in the wave of the digital revolution for a variety of economic and demographic reasons (Longley and Singleton 2009). They might experience disadvantages or limited access to these resources, digital skills and online information resulting from learning barriers, physical disabilities, and availability or affordability of IT equipment and broadband internet.
In response to the issue of digital divide and exclusion, the CDRC Mapmaker displays two sets of maps related to this theme: Broadband Speed and Availability (https://mapmaker.cdrc.ac.uk/#/broadband-speed) and Internet User Classification (https://mapmaker.cdrc.ac.uk/#/internet-user-classification). The broadband speed data are annual average wired internet speed by output area for both residential and commercial addresses published by Ofcom. The Internet User Classification, developed by Alexiou and Singleton (2018), is a demographic neighbourhood classification of 10 distinctive supergroups, ranging from “e-Cultural Creators” to “e-Withdrawn.” The classification provides insights into how people in different small areas in the UK engage with internet usage and online activities, which is based on input information from a variety of sources including the UK census population data, Oxford Internet Institute survey, and internet infrastructure data from Ofcom.
Through the map visualisation of the Internet User Classification in Mapmaker, we have identified several Local Authority Districts across the UK, which have significantly higher proportions of communities that are least engaged with the internet. Most of these districts are small coastal towns with notably larger shares of senior White British residents than the national average, which are also known as the “Silver Towns” (Lan and Longley 2021) such as Blackpool, Southend, Swansea, and Christchurch. Figure 3 takes Christchurch as an example and shows the geographic distribution of the Internet User Classification groups as well as the proportions of the LSOAs falling within each of the 10 Groups. The Settled Offline Communities Group is the modal class of the neighbourhoods, which accounts for 31.9% of the LSOAs in this district. The pen portrait of Settled Offline Communities describes members of the Group as elderly, retired White British who might have only limited or indeed no engagement with the internet. Their online activities are more likely to be conducted via computers rather than mobile devices and are perhaps restricted to information seeking and limited online shopping rather than social networking or gaming (Alexiou and Singleton 2018). In contrast, Fig. 4 displays the Classification in Oxford dominated by e-Professionals (33.3%) where the high-tech industries and motor manufacturing companies are. At the core of the city lie the Colleges of the University of Oxford, which are mostly classified as e-Cultural Creators that have high levels of Internet engagement, particularly regarding social networks, communication, streaming, and gaming.
Residential Movement and Ethnicity
Both intra- and inter-city residential movements of households provide important population dynamics to spatial and social mobility, resulting in various neighbourhood outcomes such as ethnic residential segregation (Clark and Fossett 2008). However, residential mobility and ethnic residential segregation–related studies were previously limited by the coarse temporal and spatial granularity of data sources until the recent proliferation of longitudinal population data (Coulter et al. 2016; Lan et al. 2021). The Linked Consumer Registers (Lansley et al. 2019) are among the novel data assets that have been used to study residential mobility (van Dijk et al. 2021) and segregation (Lan et al. 2021) in British society.
Making use of the georeferenced addresses and modelled ethnicity from names (Kandt and Longley 2018) in the Linked Consumer Registers, the Mapmaker presents a series of proportion maps by the census ethnic groups in the UK in the four-time points—1997, 2006, 2016 and 2020 (https://mapmaker.cdrc.ac.uk/#/modelled-ethnicity-proportions). This allows researchers, policymakers, and the public to examine the geographic distributions of specific ethnic groups overtime at the local authority level. Figure 5(a) and (b) shows the changes in the Bangladeshi communities in London by comparing the proportions and distributions of the populations over the decades. Unlike other minority groups, most Bangladeshi communities are reported to concentrate in London (Lan et al. 2021), particularly in the London Borough of Tower Hamlets coloured in dark purple in Fig. 5(a) where almost 20% of its residents in 1997 were Bangladeshis. By hovering a mouse pointer over Tower Hamlets on the map, the information in the right-side panel also indicates the proportions of the Bangladeshi residents in this Borough increased from 18% in 1997 to 21% in 2020.
Apart from Tower Hamlets, the neighbouring Borough Newham and Camden also recorded high percentages of Bangladeshi residents in 1997. In 2020, a considerable increase in the proportions of Bangladeshis has been spotted in London Boroughs further beyond the previous concentrations in Tower Hamlets and Newham in Fig. 5(b), such as Redbridge and Barking. During the recent two decades, there have been urban gentrification and regeneration projects completed in the neighbourhoods in Tower Hamlets, for instance, Shoreditch, Spitalfields, Banglatown, and Whitechapel. East Village in Stratford, developed as the 2012 London Olympic sites, has been regenerated and adapted to create new residential buildings, shopping malls and restaurants. The expansion of the Bangladeshi communities beyond the East End is likely to be connected with the residential moves and displacement of many deprived residents from the city centre (van Dijk et al. 2021) resulting from these urban redevelopment projects. However, it is worth noting that the underpinning data have the potential to scrutinise social and demographic changes since the 2011 Census at finer granularities, for example, at the neighbourhood or even household level, although they are aggregated to local authority districts in the map for disclosure control purposes.