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Most of the techniques involved in customer clustering and segmentation are based on conventional methods of quantitative analysis or traditional data mining approaches such as the K-Means algorithm. However, clustering approaches based on artificial neural networks (ANNs), evolutionary algorithms, and fuzzy methods can be more efficient since they can reveal nonlinear patterns. They also seem to be more robust in coping with noise-related issues and relevant noise handling operations. They do not make any statistical distributional assumptions regarding the nature of the data. In this article, we develop a hybrid approach based on ANNs and swarm intelligence to reveal the underlying pattern structure of customers of an insurance company in the Republic of Ireland. This model is tailored to the scope of segmenting administrative districts, or ``small areas,'' given policyholders' spatial characteristics. To that end, the geospatial features of customers are taken into account. Geodemographically speaking, by implementing such a hybrid model, the relative similarity among spatial objects (small areas in this work) are preserved. In this way, the similarity of each small area to all other small areas is characterized. Consequently, the pattern of customers is analyzed using an optimal and intelligent solution. We can also visualize the results of this study.

This article explores the geodemographic dynamics of the Russian population of the near abroad in the post-Soviet period. It analyzes the quantitative changes and transformation of its geography, the level of urbanization and the gender and age structure. The study shows that in the post-Soviet period there was a sharp decline in the number of Russians in all of the near abroad. This process was most intensive in the 1990s. The maximum demographic losses during this period were suffered by the Russian population of Transcaucasia and a number of countries in Central Asia. At the beginning of the twenty-first century, the rate and absolute scale of decline are decreasing, but the trend itself remains stable. This is associated with the natural decline of the Russian population, its migration to Russia and foreign countries, and assimilation. The relationship of these factors to the quantitative decline has varied across time and across individual countries. In almost all Russian communities a significant preponderance of women is recorded. The median age of Russians in all countries of the near abroad is more than 40 years. The level of urbanization of Russians in most of these countries has decreased. Better preserved are the metropolitan and rural Russian populations. The demographic ratio of the Russian communities of individual countries and macroregions has changed. The numbers of Russians in Kazakhstan and Ukraine (without the people's republics of Donbass) are already comparable, and there are more Russians in the Baltic countries than in Central Asia. Russian communities of unrecognized (or partially recognized) States are characterized by increased demographic stability.

This paper critically engages with the increasingly prevalent concept and practice of “urban experimentation” and associated transformations towards sustainability. Too often, the paper argues, urb...

The following review article analyzes an array of scientific studies dedicated to the Russian population of the neighboring countries. This article considers the works dealing with quantitative, spatial, and settlement dynamics of Russian communities; scale and direction of migration; shifts in indicators of natural reproduction and sex-age structure. Analysis of the scientific literature shows a widespread trend of depopulation, reduction in geography, a noticeable gender imbalance and increase in the average age among Russians across the entire near abroad. But there are also significant cross-country differences in these trends. This article also identifies main directions of research on the ethno-social dynamics of the Russian population, including analysis of the problems of its ethnic self-identification and interethnic marriage, the related assimilation processes, which, depending on the country and region, could contribute to both replenishment of Russian communities, or growth of their demographic losses. The studies dedicated to sociocultural processes, to position of the Russian language and Russian culture in the post-Soviet space, show multidirectional nature of the changes. A significant reduction in the scale of Russian-language education and other spheres of sociocultural infrastructure was combined in the post-Soviet space with dominance of the Russian-speaking Internet and keeping Russian as the language of interethnic communication. The scientific literature also reveal significant differences in the strategies of the Russian people themselves, in some regions of the neighboring countries they are predominantly focused on complex socio-cultural integration, in the others — on preservation of their cultural and linguistic specifics.

Firearm injuries (GSW) are a growing public health concern and leading cause of morbidity and mortality among children, yet predictors of injury remain understudied. This study examines the correlates of pediatric GSW within our county.We retrospectively queried an urban Level 1 trauma center registry for pediatric (0-18 years) GSW from September 2013 to January 2019, examining demographic, clinical, and injury information. We used a geographic information system to map GSW rates and perform spatial and spatiotemporal cluster analysis to identify zip code "hot spots."393 cases were identified. The cohort was 877% male, 87% African American, 10% Hispanic, and 22% Caucasian/Other. Injuries were 92% violence-related and 4% accidental, with 63% occurring outside school hours. Mortality was 12%, with 53% of deaths occurring in the resuscitation unit. Zip-level GSW rates ranged from 0 to 9 (per 1000 18 years) by incident address and 0-6 by home address. Statistically significant hot spots were in predominantly underserved African American and Hispanic neighborhoods.Geodemographic analysis of pediatric GSW injuries can be utilized to identify at-risk neighborhoods. This methodology is applicable to other metropolitan areas where targeted interventions can reduce the burden of gun violence among children.Retrospective study.Level III.

Predictive risk assessment and risk stratification models based on geodemographic postcode-based consumer classification are widely used in the pension and life insurance industry. However, these are static socio-economic models not directly related to health information. Health information is increasingly used for annuity underwriting in the UK, using health status when the annuity is purchased. In real life, people develop new health conditions and lifestyle habits and can start and stop a certain treatment regime at any time. This requires the ability to dynamically classify clients into time-varying risk profiles based on the presence of evolving health-related conditions, treatments and outcomes. We incorporate landmark analysis of electronic health records (EHR), in combination with the baseline hazards described by Gompertz survival distributions, for dynamic prediction of survival probabilities and life expectancy. We discuss a case-study based on landmark analysis of the survival experience of a cohort of 110,243 healthy participants who reached age 60 between 1990–2000.

In the article the authors studied the territorial aspect of migration of urban and rural population. The analysis of inter-regional migrations is necessary to assess the internal migration situation in the country. Based on statistical data, the analysis of the number and share of urban and rural population of the Republic of Kazakhstan was carried out. The differentiation of the population by Kazakhstan regions is studied. Based on the study of domestic sociodemographic indicators, it is concluded that migration and population are geodemographic terms that depend on each other. The analysis of the balance of interregional migration of the population of the Republic of Kazakhstan is relevant, since demographic and economic results affect countries regardless of the level of economic development. The nature of migration in Kazakhstan has changed at the present stage, as values and migration factors have been transformed. Migration processes are a concrete result of a specific stage of socio-economic and political development of a country. This is confirmed by the analysis of the population of the capital for the period from 1999 to 2019. The growth of intraregional migration ensures the growth of demographic potential. The continuous outflow of people of working age from rural areas and the return of people of pre-retirement and retirement age to rural areas ensures age transformation in rural areas, namely demographic aging of the population. This process increases the demographic burden on the working-age population and reduces the birth rate due to a drop in the number of people at the fertile age.

To relieve road congestion, a variety of transport demand management measures (TDM) have been implemented all over the world. The success of these measures has been found to depend at least partially on users’ perception about them. Several articles have jointly addressed the acceptability of public transport improvement (carrot policies) and road pricing (stick policies). However, these research works have not considered discounts on existing toll roads to relieve congestion on free alternative expressways. The objective of this paper is to study the combined acceptance of different congestion-calming policies at the suburban level, including the promotion of toll roads and measures to foster the use of transit. To that end, a survey was conducted among travelers on a commuting transport corridor in the region of Madrid aimed at exploring their perceptions towards four TDM measures, and a choice modeling framework was conducted. The scenarios considered are “carrot” policies, and the results of the analysis indicate that enhancing public transportation enjoys more support among respondents than toll promotions. Motorists using the free highway are more willing to use transit improvements than to opt for a toll road as an alternative regardless of the proposed toll discounts. Among the TDM measures explored, the least supported is the promotion of toll discounts associated with non-household carpooling. Furthermore, according to the results, the adoption of these TDM measures is more influenced by trip-related factors, in particular, the trip frequency and the mode of transport, than by socio-economic characteristics of the traveler. Finally, some geodemographic attributes of the residential location are also found to be statistically significant.

The article examines the absolute and relative increase in the population of the region, regional differences in births and deaths at the district level.

The U.S. HIV incidence has decreased or stabilized among all subgroups except for young Latino men who have sex with men (YLMSM). Previous research has suggested that Latinos experience geographic accessibility barriers to YLMSM-friendly HIV prevention services. We aimed to characterize the geographic accessibility of young Latinos ages 15-29 years to HIV preventive services in Miami-Dade County, the domestic HIV epicenter. Using ArcMap, we created a density map of 18 YLMSM-friendly HIV programs, then used Network Analysis Tools to generate service areas describing time and travel distance for walking, public transit, and driving. Our results show that accessibility to YLMSM-friendly HIV prevention services by YLMSM varies by mode of transportation. Of YLMSM, HIV prevention services are available to 2% by walking, 19% by public transit, and 70% by driving. To increase accessibility, future public health interventions should use geographic information system and geodemographic data to identify areas for culturally appropriate service expansion.

Precis Approximately one in nine Florida residents over the age of 65 years (438,642 people) live more than an hour away from a glaucoma specialist, which represents a significant barrier to care. Purpose To describe access to glaucoma care for Florida's elderly population using travel time to American Glaucoma Society (AGS) member offices. Methods For this cross-sectional service coverage analysis, a list of AGS member locations was extracted from the AGS website, and addresses were geocoded using ArcGIS Online (Esri, Redlands, CA). Driving time regions were created using the service area tool in ArcGIS Pro 2.4 (Esri, Redlands, CA) and overlaid with 2010 United States Census and 2016 American Community Survey data for all Florida residents age 65 or older. Results Fifty-eight AGS member providers with 65 locations were identified and geocoded. There were 3,797,625 individuals aged over 65 years in Florida, of which 1,153,320 (30.4%) lived within 15 minutes of driving time from an AGS provider's office, 2,586,825 (68.1%) within 30 minutes, 3,358,983 (88.4%) within 60 minutes, and 3,491,815 (91.9%) within 120 minutes. The areas with the lowest access include rural areas near Lake Okeechobee and the Florida Panhandle. The population living beyond a 60-minute drive was more likely to be white, non-Hispanic and older, but less likely to be living below the federal poverty level or receiving public assistance than the population living within a 60-minute drive. Conclusions There is a significant travel burden for the elderly community of Florida to reach AGS providers. Additional studies could help identify other social barriers to accessing glaucoma providers in Florida and beyond in an effort to improve patient compliance and, ultimately, vision outcomes.

Fuzzy geographically weighted clustering has been proposed as an approach for improving fuzzy c-means algorithm when applied to geodemographic analysis. This clustering method allows a spatial entity to belong to more than one cluster with varying degrees, namely, membership values. Although fuzzy geographically weighted clustering attempts to create geographically aware clusters, it partially fails to trace spatial dependence and heterogeneity because, as a global metric, the membership values are calculated across the entire set of spatial entities. Here we introduce the first local version of fuzzy geographically weighted clustering, ‘local fuzzy geographically weighted clustering.’ In local fuzzy geographically weighted clustering, the membership values of a spatial entity are updated only according to the membership values of the spatial entities within its neighborhood and not across the entire set of entities, as originally proposed by the global metric. Additionally, we apply particle swarm optimization meta-heuristic to overcome the random initialization problem regarding the fuzzy c-means algorithm. To evaluate our method we compare local fuzzy geographically weighted clustering to global fuzzy geographically weighted clustering using a cancer incident benchmark dataset for Manhattan, New York. The results show that local fuzzy geographically weighted clustering outperforms the global version in all experimental settings.

Background Emergency diagnosis of cancer is associated with poorer short-term survival and may reflect delayed help-seeking. Optimal targeting of interventions to raise awareness of cancer symptoms is therefore needed. Methods We examined the risk of emergency presentation of lung and colorectal cancer (diagnosed in 2016 in England). By cancer site, we used logistic regression (outcome emergency/non-emergency presentation) adjusting for patient-level variables (age, sex, deprivation and ethnicity) with/without adjustment for geodemographic segmentation (Mosaic) group. Results Analysis included 36 194 and 32 984 patients with lung and colorectal cancer. Greater levels of deprivation were strongly associated with greater odds of emergency presentation, even after adjustment for Mosaic group, which nonetheless attenuated associations (odds ratio [OR] most/least deprived group = 1.67 adjusted [model excluding Mosaic], 1.28 adjusted [model including Mosaic], P Conclusion Variation in risk of emergency presentation in cancer patients can be explained by geodemography, additional to deprivation group and age. The findings support proof of concept for public health interventions targeting all the examined attributes, including geodemography.

This article explores the geodemographic dynamics of the Russian population of the near abroad in the post-Soviet period. It analyzes the quantitative changes and transformation of its geography, the level of urbanization and the gender and age structure. The study showed that in the post-Soviet period there was a sharp decline in the number of Russians in all neighboring countries. This process was most intensive in the 1990s. The maximum demographic losses during this period were suffered by the Russian population of Transcaucasia and a number of countries in Central Asia. At the beginning of the twenty-first century, the rate and absolute scale of decline are decreasing, but the trend itself remains stable. This is associated with the natural decline of the Russian population, its migration to Russia and foreign countries, and assimilation. The relationship of these factors to the quantitative decline has varied across time and across individual countries. In almost all Russian communities, a significant preponderance of women is recorded. The median age of Russians in all neighboring countries is more than 40 years. The level of urbanization of Russians in most neighboring countries has decreased. Better preserved are the metropolitan and rural Russian populations. The demographic ratio of the Russian communities of individual countries and macroregions has changed. The number of Russians in Kazakhstan and Ukraine (without the people's republics of Donbass) is already comparable, and there are more Russians in the Baltic countries than in Central Asia. Russian communities of unrecognized (partially recognized) states are characterized by increased demographic stability.

Geodemographic classifications are used to understand social phenomena. Within the private sector, for business planning accounting for underlying spatial differences in economic, social and demographic composition of geographical areas. In the public sector geodemographics is used in health, local governance and social research. Bespoke geodemographic systems help public health authorities to target neighbourhoods most at need of health campaigns. Geodemographic classifications are invariably cross-sectional and static. Having comparable geodemographic classifications over a period of time will help demonstrate changes in socio-economic and demographic structures. A time-comparable geodemographic scheme can bring out changes in multivariable compositional characteristics which are otherwise hidden by cross-sectional measures. This paper reports on variable selection, the conversion of inputs to a consistent geography and the creation of directly comparable geodemographic classifications of small areas across England and Wales for 1991, 2001 and 2011. Changes and stability in area characteristics are then analysed. The results show that most neighbourhoods are allocated into the same area type over time. Where there is change, this can be themed as: Socio-economic polarisation, characterised by a decrease in neighbourhoods found in the middle of the socio-economic spectrum, with an increase in number of areas at either end; Growth in the number of neighbourhoods with non-White ethnic residents, mainly Black ethnic minorities and Asian based communities and; Reorganisation and increase in classifications relating to urban areas, signalling development or growth of metropolitan areas.

Purpose Drawing from the contingency model, this study aims to investigate the moderating effects of business-to-business (B2B) buyer personal characteristics on the relationship between sales activities and sales effectiveness. Design/methodology/approach As an application of engaged scholarship, this study leverages a years’ worth of sales activity and results from a Fortune 500 financial services company for 2,710 dyads; personal characteristics (i.e. geodemographics) were appended for the customers/prospects of the dyads. The data was analyzed with hierarchical regression, and subgroups were tested using the Chow test. Findings The results support that geodemographic segments – as a proxy for personal characteristics – moderate the strength of the relationship between selling activities and sales effectiveness. Overall, the results demonstrate that selling activities have varying impacts on sales effectiveness within geodemographic segments and buyclass scenarios. Practical implications While it has been long held that understanding the personal characteristics of the B2B purchasing decision-maker is critical for sales effectiveness, little guidance has been provided on how to accomplish this to scale. The present study provides a framework and process for practitioner operationalization. Originality/value This research contributes to the literature that has explored personal characteristics of buying center members. Additionally, the results suggest that personal characteristics of the purchase decision-maker may transcend business-to-consumer and B2B purchasing contexts.

Businesses today face great competition in their operations, making it necessary for them to adopt a “customer-oriented” approach. In this competitive environment, where customers are more valuable, enterprises accrue great advantages from an understanding of the characteristics of the target audience in all dimensions. This is where the importance of geo-marketing and demographic segmentation for enterprises emerges. This study performed a geo-demographic segmentation of the urban neighbourhoods of Eskişehir province and sought to determine the characteristics of the people living in these neighbourhoods at the household level. The Groups created using the SPSS package program as well as Principal Components Analysis (PCA) and Hierarchical Clustering Analysis were then mapped on the GIS platform as urban neighbourhoods.

The article explores and presents the leading trends in population dynamics and spatial distribution in Blagoevgrad municipality, Bulgaria. The key demographic aspects were analysed for the period between 2001 and 2016 (number and density of rural and urban population, natural and migratory dynamics and age structure). Data was freely provided by the National Institute of Statistics, Bulgaria and collected during field work. Specific features of the demographic resources were shown and the processes of depopulation and suburbanization were explained and localized. Their impact on the settlements in the area under study was analyzed - a large administrative and educational centre, Blagoevgrad town and 25 villages. Results show that the contemporary geodemographic processes and the territorial distribution of population are also features describing other municipalities in Bulgaria.

We estimate the maximum capability to reduce CO2 by substituting private car travel for e-bike. We use spatial microsimulation (population-synthesis) to stimulate the adult population within every small area in England, taking account of area type and geodemographic circumstances of the population. By estimating for individuals the distance they are capable of travelling by e-bike and the extent to which they are capable of replacing private car travel, we find the upper limit on the capability to reduce CO2 by substituting car travel for e-bike use is 24.4 MTCO2 p.a. (per annum) in England. CO2 saving capability per person and per small area are highest (over 750 kg CO2 per person p.a.) for residents of rural areas and the rural urban fringe. e-bikes offer major conurbations more modest CO2 saving capability per person. We identify areas which are vulnerable to car related economic stress and also have high capability to replace car km with e-bikes, which if supported appropriately could contribute to equitable carbon reduction. Though capable of a very significant contribution to transport carbon reduction, other changes in technology and reduction in demand would also be necessary to reach zero emissions. Our results are directly relevant to policy actors internationally who require evidence on place-based decarbonisation capability, particularly where car dependence is high. The results highlight how context is important in any attempt to design policy for equitable carbon reduction both to influence discussion on what is possible, as well as practical identification of areas for targeted intervention. Digital indicators covering all zones in a country's geography such as this are also useful because of the rapid digitalisation of policy making. We provide code so that others can produce similar analyses in other countries ( https://github.com/DrIanPhilips/e-bikeCarbonReductionCapability ).

Palestinian healthcare institutions face difficulties in providing effective service delivery, particularly in times of crisis. Problems arising from inadequate healthcare service delivery are traceable to issues such as spatial coverage, emergency response time, infrastructure, and manpower. In the Gaza Strip, specifically, there is inadequate spatial distribution and accessibility to healthcare facilities due to decades of conflicts. This study focuses on identifying hospital site suitability areas within the Gaza Strip in Palestine. The study aims to find an optimal solution for a suitable hospital location through suitability mapping using relevant environmental, topographic, and geodemographic parameters and their variable criteria. To find the most significant parameters that reduce the error rate and increase the efficiency for the suitability analysis, this study utilized machine learning methods. Identification of the most significant parameters (conditioning factors) that influence a suitable hospital location was achieved by employing correlation-based feature selection (CFS) with the search algorithm (greedy stepwise). Thus, the suitability map of potential hospital sites was modeled using a support vector machine (SVM), multilayer perceptron (MLP), and linear regression (LR) models. The results of the predicted sites were validated using CFS cross-validation and the receiver operating characteristic (ROC) curve metrics. The CFS analysis shows very high correlations with R2 values of 0.94, 0. 93, and 0.75 for the SVM, MLP, and LR models, respectively. Moreover, based on areas under the ROC curve, the MLP model produced a prediction accuracy of 84.90%, SVM of 75.60%, and LR of 64.40%. The findings demonstrate that the machine learning techniques used in this study are reliable, and therefore are a promising approach for assessing a suitable location for hospital sites for effective health delivery planning and implementation.

This article outlines the need for a general overview of the geodemographic state of several indicators in order to see the emerging socio-economic processes. The presentation shows that these countries have identical demographic and social problems, with the possible exception of the Albanians. Although the trend is emerging in all probability and they are facing similar trends with other countries. The derivation of the general geodemographic picture is a reason for the region to choose the implementation of European integration policies and programs that are consistent with the regional geodemographic approach of individual countries. In practice, bringing geodemographic problems to the forefront can be a successful milestone in the rapid and effective integration of these countries into the European Union.

The COVID-19 pandemic caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) continues to impact the United States. While age and comorbid health conditions remain primary concerns in the community-based transmission of the virus, empirical evidence continues to suggest that substantial variability exists in the geographic and geodemographic distribution of COVID-19 infection rates. The purpose of this paper is to provide an alternative, spatiotemporal perspective on the pandemic using the state of Wisconsin as a case study. Specifically, in this paper, we explore the geographic nuances of COVID-19 and its spread in Wisconsin using a suite of spatial statistical approaches. We link detected hot spots of COVID-19 to local geodemographic profiles and the presence of high-risk facilities, including federal and state correctional facilities. The results suggest that the virus disproportionately impacts several communities and geodemographic groups and that proximity to risky facilities correlates to increased community infection rates.

This article describes the demographic situation in Kashkadarya region, which has a unique natural increase in the geographical location of the population of Uzbekistan. The geodemographic aspects of the region are analyzed at the district level, the location of the population and the factors influencing it are described.

The COVID-19 pandemic has caused effects in many sectors, including in businesses and enterprises. The most vulnerable businesses to COVID-19 are micro, small, and medium enterprises (MSMEs). Therefore, this paper aims to analyze the business vulnerability of MSMEs in Indonesia using the fuzzy spatial clustering approach. The fuzzy spatial clustering approach had been implemented to analyze the social vulnerability to natural hazards in Indonesia. Moreover, this study proposes the Flower Pollination Algorithm (FPA) to optimize the Fuzzy Geographically Weighted Clustering (FGWC) in order to cluster the business vulnerability in Indonesia. We performed the data analysis with the dataset from Indonesia’s national socioeconomic and labor force survey (SUSENAS and SAKERNAS). We first compared the performance of FPA with traditional FGWC, as well as several known optimization algorithms in FGWC such as Artificial Bee Colony, Intelligent Firefly Algorithm, Particle Swarm Optimization, and Gravitational Search Algorithm. Our results showed that FPAFGWC has the best performance in optimizing the FGWC clustering result in the business vulnerability context. We found that almost all of the regions in Indonesia outside Java Island have vulnerable businesses. Meanwhile, in most of Java Island, particularly the JABODETABEK area that is the national economic backbone, businesses are not vulnerable. Based on the results of the study, we provide the recommendation to handle the gap between the number of micro and small enterprises (MSMEs) in Indonesia.

The link between the built environment and residential segregation has long been of interest to the discussion for sustainable and socially resilient cities. However, direct assessments on how extensively diverse built environments affect the social landscapes of cities at the neighborhood level are rare. Here, we investigate whether neighborhoods with a diverse built environment also exhibit different socio-economic profiles. Through a geodemographic approach, we scrutinize the socio-economic composition of Shanghai’s neighborhoods. We statistically compare the top 10% (very high values) to the bottom 10% (very low values) of the following built environment variables: density, land use mix, land use balance, and greenness. We show that high-density areas have three times the percentage of divorced residents than low-density areas. Neighborhoods with a high level of greenness have median values of 30% more residents aged between 25–44 years old and five times the percentage of houses between 60 to 119 m2 than low-greenness areas. In high land-use mix areas, the share of people that live on a pension is 30% more than the low land-use mix areas. The findings of this study can be used to improve the designs of modern, sustainable cities at the neighborhood level, significantly improving quality of life.

Like many other cosmopolitan neighbourhoods around the world, several neighbourhoods in East London have experienced rapid social and demographic change through gentrification. This chapter harnesses linked consumer and administrative data collected over a 20-year period to measure the geodemographic changes that have occurred in three neighbourhoods in London’s city fringe: Hoxton East and Shoreditch, Spitalfields and Banglatown, and Whitechapel. Using an address-level linked database, representative of the vast majority of the adult population in the United Kingdom, we produce highly granular estimations of geodemographic characteristics such as ethnicity and we characterise residential moves by their origins and destinations.

The study of geodemographic processes is the most important element of the tasks of socio-economic development of territories of different levels and the country as a whole. The article is devoted to identifying the development trends of the geodemographic situation of the administrative unit of the region - Kargapol district of the Kurgan region. They remain largely negative despite the measures taken to support the population by the authorities at all levels. The trends were determined on the basis of an analysis of population dynamics, fertility rates - general, special and age-specific birth rates, mortality rates - total and infant mortality rates, values of the natural and mechanical movement of the population. These indicators are interconnected with each other and with the age and sex structure of the population and the structure of the population as a productive force, i.e. labor resources. To analyze the age structure of labor resources, we used the coefficient of the total demographic load of the able-bodied population, and the coefficient of labor replacement. The conclusion is made about the deepening of crisis phenomena in the geodemographic situation, which requires additional measures by influencing precisely those processes that have the most negative consequences.

The article deals with the main problems of spatial development of modern Albania. It is argued that the study of Albania by Russian socio-economic geography has paid little attention. In the Russian mass consciousness, this country is surrounded by a lot of inventions that have nothing to do with reality: one of these inventions is the perception of Albania as a purely Muslim country and in general the outpost of the «Islamic world». In fact, Albania is a multi-religious state, but religious differences do not matter much for Albanians. In the geodemographic development of modern Albania there is a steady increase in the share of the center of the country, which now exceeds 50 % of the population. As a Center, the authors consider two main cities, Tirana and Durres, that does not correlate with the official zoning of Albania. The sharp increase in the share of the Center shows the similarity of Albania with Asian states, and in general it can be argued that Albania geographically belongs to Europe, and socially, demographically and historically is closer to Asia. The spatial structure of Albania's economy is characterized by an increase in the share of the Center with a decrease in its economic efficiency, as well as the lag of the North from the South, due to the factor of economic and geographical position. The realization of the idea of "Great Albania", connected with the artificiality of the state borders defined in 1913, is extremely unprofitable for modern Albania in economic, social and demographic terms.

To determine whether geodemographic factors, specifically age, gender or commuting distance, affect the attendance rates of patients referred to a Diabetic Retinopathy Treatment (DRT) Centre from the Irish National Diabetic Retinal Screening (DRS) Programme. A retrospective analysis of the first 1200 patients who were referred for ophthalmic assessment between November 2013 and June 2015 to Cork University Hospital’s (CUH) DRT clinic from the DRS was completed. A total of 972 (81%) patients attended all their scheduled DRT appointments. The attendance rates for those living in counties Cork and Kerry were 83.9% and 72.5%, respectively. Fifty-seven (4.75%) patients failed to attend any of their clinic appointments (2.2% of Cork and 11.8% of Kerry referrals). Patients living within 60 km of the treatment centre were more likely to attend (p = 0.013). Both age and gender were also shown to have a statistically significant impact on attendance, with those under 40 years of age (p

The actual functions of a region may not reflect the intent of the original zoning scheme from planners. To identify the actual urban functional regions, numerous methods have been proposed with computational advancement. Specifically, remote sensing by image recognition, geodemographic classification, social sensing with big data and geo-text mining techniques have been widely applied. Points-of-interest (POIs) are one of the most common open-access data type used to extract information pertaining to functional zones. However, previous works have either lost sight or did not make full use of the spatial interactions that can be extracted from POIs due to model limitations in the context of geographical space. In this research, we introduced an approach that detects functional regions at the scale of a neighborhood area (NA) by combining POI data and a simplified Place2vec model, which is theorized from the first law of geography. First, the POI-based spatial context is constructed by using the nearest neighbor approach. Then, we can increase the number of training tuples (tcenter, tcontext) based on the weight derived from the distance between the POI tcenter and POI tcontext. Next, high-dimensional characteristic vectors of the POIs are extracted by using the skip-gram training framework. By summarizing the POI vectors at the NA level, we employ a K-means clustering model to cluster the functional regions. Compared with other probabilistic topic models (PTMs) and Word2vec, the Place2vec-based approach obtained the highest mean reciprocal rank value (MRR-SWP=0.356, MRR-SLC=0.401, MRR-SJC=0.433, and MRR-SLin=0.421) in terms of similarity capturing performance and functional region identification accuracy (OA=0.7424). The research has important implications to urban planning and governance.

The purpose of this paper is justification of methodological peculiarities of the development trajectories modeling in the multidimensional space from the positions of social geography; their practical application (geodemographic process in Kharkiv region is taken as an example). The object of social geography is social and geographical system and social and geographical process. Combination of natural, social and economic systems in it creates a unique variety of features and properties requiring consideration of a large number of these systems parameters. According to the geographical, system and synergetic approach all these processes are described in hundreds of parameters. Adequate and correct mathematical processing of such actual material is possible only with use of virtual multidimensional geographic space where the mathematical model of the real development trajectory of the object or process is offered. The main estimation parameters are given, in particular the main diagonal in the hypercube of the multidimensional space is considered as the standard of optimal development. Comparison of the real trajectory with the optimal, determining the projection of the vector of development on the optimal trajectory, deviations from the optimal trajectory contain information on the peculiarities of the process. The proposed method was used for modeling of the geodemographic development trajectory in districts of Kharkiv region for 2002-2015 on the basis of 72 initial statistical parameters which are characterizing this process to demonstrate its peculiarities on the results of certain studies. The grouping of the districts of Kharkiv region, temporal peculiarities are shown, the phases of progressive and regressive demographic development for the studied period are established taking into account a considerable amount of the initial data. Modeling the development path allows us to define a clear perspective, to model and predict further development, to plan the pace of development. It is also a convenient monitoring tool; it also helps to develop scientifically-based management measures, etc.

The aim of this study was to associate lifestyle characteristics with COVID-19 infection and mortality rates at the U.S. county level and sequentially map the impact of COVID-19 on different lifestyle segments. We used analysis of variance (ANOVA) statistical testing to determine whether there is any correlation between COVID-19 infection and mortality rates and lifestyles. We used ESRI Tapestry LifeModes data that are collected at the U.S. household level through geodemographic segmentation typically used for marketing purposes to identify consumers’ lifestyles and preferences. According to the ANOVA analysis, a significant association between COVID-19 deaths and LifeModes emerged on 1 April 2020 and was sustained until 30 June 2020. Analysis of means (ANOM) was also performed to determine which LifeModes have incidence rates that are significantly above/below the overall mean incidence rate. We sequentially mapped and graphically illustrated when and where each LifeMode had above/below average risk for COVID-19 infection/death on specific dates. A strong northwest-to-south and northeast-to-south gradient of COVID-19 incidence was identified, facilitating an empirical classification of the United States into several epidemic subregions based on household lifestyle characteristics. Our approach correlating lifestyle characteristics to COVID-19 infection and mortality rate at the U.S. county level provided unique insights into where and when COVID-19 impacted different households. The results suggest that prevention and control policies can be implemented to those specific households exhibiting spatial and temporal pattern of high risk.

Background Road safety campaigns tend to be nationally focussed, targeting all drivers, however these are general and often ineffective in reaching drivers causing serious road injury collisions. This study aimed to identify culpable drivers involved in collisions and consider the potential of delivering focused road safety campaigns to reduce serious injury on the roads. Methods This study linked UK police data (STATS19) with hospital trauma data from the Trauma Audit Research Network (TARN) to identify serious (MAIS3+) injury collisions and the drivers involved for the county of Cambridgeshire (UK). These drivers were then assessed to be culpable, contributory or non-culpable for the serious injury collision. Additionally, geodemographic profiles were identified for the drivers using ACORN (A (UK) Classification of Residential Neighbourhoods) to establish if culpable drivers differed from non-culpable drivers. ACORN uses postcodes to differentiate areas by wealth (6 categories), available money (18 groups) and household description (62 types). Results A total of 399 Cambridgeshire drivers were profiled, with 276 drivers considered culpable or contributing to the collision and 123 non-culpable. The ACORN categories and groups for the proportions of culpable and non-culpable drivers were similar. However, within the household types differences existed with culpable drivers having higher proportions of semi-skilled workers living in traditional neighbourhoods. In contrast non-culpable drivers had higher proportions of larger families living in rural areas. Conclusion The findings suggest there are differences in specific household types. Using additional marketing tools, targeted road safety education campaigns could be aimed at culpable drivers living in these households

Geodemographics is a tool to summarize the characteristics of spatial units based on socio-economic data. It has been used over several decades to classify the characteristics of areas based on the similarities in such data, generally working by identifying groups or clusters of similar areas. It has seen use in the academic and private sectors but mostly became popular by 1980s for market research purposes – this rise in use drew attention to many issues that needed to be addressed in the literature. The purpose of this paper is to provide a geodemographic classification for the smallest scale administrative units in Ireland (Small Areas) based on the latest 2016 census population data. A further aim is to support reproducible research by following the methodology of an earlier study for the 2011 Census and to bring up the opportunity to compare the cluster results between the 2011 and 2016 census data. The comparison output provides useful insights for policy discussions in the Irish context. One of the other focal points of this study is to bring some clarity regarding some criticisms of geodemographics from an academic perspective. The paper also argues for the use of open data, open source software and supports open analysis.

This chapter explores market segmentation as well as how tourism and hospitality marketers can position their offerings to gain a competitive advantage in the marketplace. It begins with an explanation of market segmentation, which is essential for designing and implementing a marketing mix. It considers how tourism and hospitality companies decide on which market segments to focus their marketing efforts. It also discusses the criteria used by the marketer to ensure effective segmentation: using demographic, psychological, and behavioural criteria. Further, it discusses the process of target marketing and the different approaches that a tourism and hospitality organisation can take. The chapter concludes with a discussion of market positioning and how companies can position their offerings to gain a competitive advantage. Lastly, the chapter’s case study examines these principles in the context of Moxy Hotels, designed to capture the millennial traveller market, and owned by Marriott Hotels International Group.

The focus in this chapter is on the relationship between migration and neighborhood change. The chapter reviews the relationship between urban deprivation, residential mobility patterns, and urban regeneration policy, commenting on theoretical concepts and empirical findings in earlier studies. It makes extensive use of small area statistics from the UK Population Census, linking together migration data at the most detailed spatial level, Output Areas, with a geodemographic classification system. This provides some interesting insights about the structure of residential mobility in urban areas. The chapter proceeds to examine the impact of urban policy in the Merseyside region, as part of the EU Objective One programme. It uses a matched comparison method to examine the impact of the Pathways area-based initiative upon migration flows to and from Pathways Areas. Its key finding is that the Pathways programme had a significant impact upon residential stability.

Background: The floating population in urban China is facing multiple barriers to access to comprehensive, affordable, and culturally effective public health services. However, little is known about the role of geodemographic factors. This study aimed to assess the associations between geodemographic factors and access to public health services among the Chinese floating population.Methods: This study employed the data from the 2015 Migrant Dynamic Monitoring Survey data in China. Descriptive statistical analysis and principal component analysis were used to provide basic characteristics of the main variables. Multiple logistic models were used to analyze how province-level units, economic regions, and economic zones had significant associations with residential health records establishment, social medical insurance, and types and methods of health knowledge attainment in urban China. Using multiple indicator multiple cause models, the association between geodemographic factors and types and methods of health knowledge attainment was studied.Results: The results indicated that there was regional unbalance in the case of residential health records. Regional differences were significantly associated with social medical insurance. Provincial differences were significantly associated with health knowledge attainment. There were regional differences in the methods of health knowledge attainment. In the most provincial units, geodemographic factors had significant associations with types and methods of health knowledge attainment.Conclusions: This study confirmed empirical associations between geodemographic factors and access to public health services among Chinese floating population. The relevant suggestion was that provincial units with less-developed public health services should enhance their capabilities to equalize public health services.