Business opportunities analysis using GIS: the retail distribution sector

[EN] The retail distribution sector is facing a difficult time as the current landscape is characterized by ever-increasing competition. In these conditions, the search for an appropriate location strategy has the potential to become a differentiating and competitive factor. Although, in theory, an increasing level of importance is placed on geography because of its key role in understanding the success of a business, this is not the case in practice. For this reason, the process outlined in this paper has been specifically developed to detect new business locations. The methodology consists of a range of analyzes with Geographical Information Systems (GISs) from a marketing point of view. This new approach is called geomarketing. First, geodemand and geocompetition are located on two separate digital maps using spatial and non-spatial databases. Second, a third map is obtained by matching this information with the demand not dealt with properly by the current commercial offer. Third, the Kernel density allows users to visualize results, thus facilitating decision-making by managers, regardless of their professional background. The advantage of this methodology is the capacity of GIS to handle large amounts of information, both spatial and non-spatial. A practical application is performed in Murcia (Spain) with 100 supermarkets and data at a city block level, which is the highest possible level of detail. This detection process can be used in any commercial distribution company, so it can be generalized and considered a global solution for retailers.Roig Tierno, H.; Baviera-Puig, A.; Buitrago Vera, JM. (2013). Business opportunities analysis using GIS: the retail distribution sector. Global Business Perspectives. 1(3):226-238. doi:10.1007/s40196-013-0015-6S22623813Alarcón, S. (2011). The trade credit in the Spanish agrofood industry. Mediterranean Journal of Economics, Agriculture and Environment (New Medit), 10(2), 51–57.Alcaide, J. C., Calero, R., & Hernández, R. (2012). Geomarketing. Marketing territorial para vender y fidelizar más. Madrid: ESIC.Applebaum, W., & Cohen, S. B. (1961). The dynamics of store trading areas and market equilibrium. Annals of the Association of American Geographers, 51(1), 73–101.Baviera-Puig, A., Buitrago-Vera, J. M., Escriba, C., & Clemente, J. S. (2009). Geomarketing: Aplicación de los sistemas de información geográfica al marketing. Paper presented at the Octava Conferencia Iberoamericana en Sistemas, Cibernética e Informática, Orlando, FL.Baviera-Puig, A., Buitrago-Vera, J. M., & Mas-Verdú, F. (2012). Trade areas and knowledge-intensive services: The case of a technology centre. Management Decision, 50(8), 1412–1424.Baviera-Puig, A., Buitrago-Vera, J. M., & Rodríguez-Barrio, J. E. (2013). Un modelo de geomarketing para la localización de supermercados: Diseño y aplicación práctica. Documentos de Trabajo de la Cátedra Fundación Ramón Areces de Distribución Comercial (DOCFRADIS), 1, 1–27.Berumen, S. A., & Llamazares, F. (2007). La utilidad los métodos de decisión multicriterio (como el AHP) en un entorno de competitividad creciente. Cuadernos de administración, 20(34), 65–87.Birkin, M., Clarke, G., & Clarke, M. (2002). Retail geography and intelligent network planning. Chichester: Wiley.Chasco, C. (2003). El geomarketing y la distribución commercial. Investigación y Márketing, 79, 6–13.Chen, R. J. C. (2007). Significance and variety of geographic information system (GIS) applications in retail, hospitality, tourism, and consumer services. Journal of Retailing and Consumer Services, 14, 247–248.Church, R. L. (2002). Geographical information systems and location science. Computers and Operations Research, 29, 541–562.Church, R. L., & Murray, A. T. (2009). Business site selection, location analysis and GIS. Hoboken, NJ: Wiley.Clarke, G. (1998). Changing methods of location planning for retail companies. GeoJournal, 45, 289–298.Clarkson, R. M., Clarke-Hill, C. M., & Robinson, T. (1996). UK supermarket location assessment. International Journal of Retail and Distribution Management, 24(6), 22–33.Davis, P. (2006). Spatial competition in retail markets: Movie theaters. The RAND Journal of Economics, 37(4), 964–982.Ghosh, A., & McLafferty, S. L. (1982). Locating stores in uncertain environments: A scenario planning approach. Journal of Retailing, 58(4), 5–22.Härdle, W. (1991). Smoothing techniques with implementation in S. Nueva York, NY: Springer.Harris, B., & Batty, M. (1993). Locational models, geographical information, and planning support systems. Journal of Planning Education and Research, 12, 184–198.Hernandez, T. (2007). Enhancing retail location decision support: The development and application of geovisualization. Journal of Retailing and Consumer Services, 14, 249–258.Hernandez, T., & Bennison, D. (2000). The art and science of retail location decisions. International Journal of Retail and Distribution Management, 28(8), 357–367.Huff, D. (1963). Defining and estimating a trade area. Journal of Marketing, 28, 34–38.Instituto Nacional de Estadística (INE). (2011). Padrón de habitantes 2011. http://www.ine.es . Accessed 9 Oct 2012.Kelly, J. P., Freeman, D. C., & Emlen, J. M. (1993). Competitive impact model for site selection: The impact of competition, sales generators and own store cannibalization. The International Review of Retail, Distribution and Consumer Research, 3, 237–259.Latour, P., & Le Floc’h, J. (2001). Géomarketing: Principes, méthodes et applications. París: Éditions d’Organisation.Mendes, A. B., & Themido, I. H. (2004). Multi-outlet retail site location assessment. International Transactions in Operational Research, 11, 1–18.Moreno, A. (1991). Modelización cartográfica de densidades mediante estimadores Kernel. Treballs de la Societat Catalana de Geografia, 6(30), 155–170.Moreno, A. (2007). Obtención de capas raster de densidad. In A. Moreno (Coord.), Sistemas y Análisis de la información Geográfica. Manual de autoaprendizaje con ArcGIS (pp. 685–691). Madrid: Editorial RA-MA.Murad, A. A. (2003). Creating a GIS application for retail centers in Jeddah City. International Journal of Applied Earth Observation and Geoinformation, 4, 329–338.Murad, A. A. (2007). Using GIS for retail planning in Jeddah City. American Journal of Applied Sciences, 4(10), 820–826.Musyoka, S. M., Mutyauvyu, S. M., Kiema, J. B. K., Karanja, F. N., & Siriba, D. N. (2007). Market segmentation using geographic information systems (GIS). A case study of the soft drink industry in Kenya. Marketing Intelligence and Planning, 25(6), 632–642.Nielsen Database. (2012). Retailers Database. http://www.nielsen.com/global/en.html . Accessed 12 Oct 2012.Ozimec, A. M., Natter, M., & Reutterer, T. (2010). Geographical information systems-based marketing decisions: Effects of alternative visualizations on decision quality. Journal of Marketing, 74, 94–110.Reilly, W. J. (1931). The law of retail gravitation. New York: Knickerbocker Press.Rob, M. A. (2003). Some challenges of integrating spatial and non-spatial datasets using a geographical information system. Information Technology for Development, 10, 171–178.Rosenblatt, M. (1956). Remarks on some nonparametric estimates of a density functions. Annals of Mathematical Statistic, 27, 832–837.Sede Electrónica del Catastro. (2012). Datos Catastrales. https://www.sedecatastro.gob.es . Accessed 10 Oct 2012.Silverman, B. W. (1986). Density estimation for statistics and data analysis. London: Chapman and Hall.Sleight, P., Harris, R., & Webber, R. (2005). Geodemographics, GIS and neighbourhood targeting. Chichester: Wiley.Suárez-Vega, R., Santos-Peñate, D. R., & Dorta-González, P. (2012). Location models and GIS tools for retail site location. Applied Geography, 35, 12–22.Thaler, R. (1986). The psychology and economics conference handbook: Comments on Simon, on Einhorn and Hogarth, and on Tversky and Kahneman. The Journal of Business, 59(4), 279–284.Wood, S., & Reynolds, J. (2012). Leveraging locational insights within retail store development? Assessing the use of location planners’ knowledge in retail marketing. Geoforum, 43, 1076–1087

Small but crucial : the novel small heat shock protein Hsp21 mediates stress adaptation and virulence in Candida albicans

Peer reviewedPublisher PD

A phase II study of paclitaxel and capecitabine as a first-line combination chemotherapy for advanced gastric cancer

Paclitaxel and capecitabine, which have distinct mechanisms of action and toxicity profiles, have each shown high activity as single agents in gastric cancer. Synergistic interaction between these two drugs was suggested by taxane-induced upregulation of thymidine phosphorylase. We, therefore, evaluated the antitumour activity and toxicities of paclitaxel and capecitabine as first-line therapy in patients with advanced gastric cancer (AGC). Patients with histologically confirmed unresectable or metastatic AGC were treated with capecitabine 825 mg m−2 p.o. twice daily on days 1–14 and paclitaxel 175 mg m−2 i.v. on day 1 every 3 weeks until disease progression or unacceptable toxicities. Between June 2002 and May 2004, 45 patients, of median age 57 years (range=38–73 years), were treated with the combination of capecitabine and paclitaxel. After a median 6 cycles (range=1–9 cycles) of chemotherapy, 43 were evaluable for toxicity and response. A total of 2 patients showed complete response and 20 showed partial response making the overall response rate 48.9% (95% CI=30.3–63.5%). After a median follow-up of 42.2 months (range=31.2–54.3 months), median time to progression was 5.6 months (95% CI=3.9–7.2 months) and median overall survival was 11.3 months (95% CI=8.1–14.4 months). Grade 3 or 4 adverse events include neutropaenia (46.5% of patients), hand–foot syndrome (9.3%), arthralgia (9.3%), and asthenia (4.7%). There was no neutropaenic fever or treatment-related deaths. Paclitaxel and capecitabine combination chemotherapy was active and highly tolerable as a first-line therapy for AGC

Long-Term Follow-Up of Transsexual Persons Undergoing Sex Reassignment Surgery: Cohort Study in Sweden

CONTEXT: The treatment for transsexualism is sex reassignment, including hormonal treatment and surgery aimed at making the person's body as congruent with the opposite sex as possible. There is a dearth of long term, follow-up studies after sex reassignment. OBJECTIVE: To estimate mortality, morbidity, and criminal rate after surgical sex reassignment of transsexual persons. DESIGN: A population-based matched cohort study. SETTING: Sweden, 1973-2003. PARTICIPANTS: All 324 sex-reassigned persons (191 male-to-females, 133 female-to-males) in Sweden, 1973-2003. Random population controls (10:1) were matched by birth year and birth sex or reassigned (final) sex, respectively. MAIN OUTCOME MEASURES: Hazard ratios (HR) with 95% confidence intervals (CI) for mortality and psychiatric morbidity were obtained with Cox regression models, which were adjusted for immigrant status and psychiatric morbidity prior to sex reassignment (adjusted HR [aHR]). RESULTS: The overall mortality for sex-reassigned persons was higher during follow-up (aHR 2.8; 95% CI 1.8-4.3) than for controls of the same birth sex, particularly death from suicide (aHR 19.1; 95% CI 5.8-62.9). Sex-reassigned persons also had an increased risk for suicide attempts (aHR 4.9; 95% CI 2.9-8.5) and psychiatric inpatient care (aHR 2.8; 95% CI 2.0-3.9). Comparisons with controls matched on reassigned sex yielded similar results. Female-to-males, but not male-to-females, had a higher risk for criminal convictions than their respective birth sex controls. CONCLUSIONS: Persons with transsexualism, after sex reassignment, have considerably higher risks for mortality, suicidal behaviour, and psychiatric morbidity than the general population. Our findings suggest that sex reassignment, although alleviating gender dysphoria, may not suffice as treatment for transsexualism, and should inspire improved psychiatric and somatic care after sex reassignment for this patient group

Psychosocial adaptation of adolescent migrants in a Swiss community survey

OBJECTIVE: The aim of this study was to compare psychosocial adaptation in adolescent (first generation) migrants, double-citizens (mainly second generation with one migrant parent), and native Swiss, and to compare migrants from various European regions. METHOD: Data from a community survey were based on 1,239 participants (mean age 13.8, SD = 1.6 years) with 996 natives, 55 double-citizens, and 188 migrants. The adolescents completed the youth self-report measuring emotional and behavioural problems, and various questionnaires addressing life events, personality variables, perceived parental behaviour (PPB), family functioning, school environment, and social network. RESULTS: Adolescent migrants had significantly higher scores for internalizing and externalizing problems. There was a pattern of various unfavourable psychosocial features including life events, coping, self-related cognitions, and PPB that was more common among adolescent migrants than natives. Double-citizens were similar to natives in all domains. Young adolescents from South and South-East Europe differed from natives in terms of more unfavourable psychosocial features. Migrant status was best predicted by adverse psychosocial features rather than emotional and behavioural problems. CONCLUSION: There is some indication that certain migrant adolescents are at risk of psychosocial mal-adaptation. Obviously, ethnic origin is an important moderator

Genetic and environmental influences on the stability of withdrawn behavior in children: A longitudinal, multi-informant twin study.

We examined the contribution of genetic and environmental influences on the stability of withdrawn behavior (WB) in childhood using a longitudinal multiple rater twin design. Maternal and paternal ratings on the withdrawn subscale of the Child Behavior Checklist (CBCL) were obtained from 14,889 families when the twins were 3, 7, 10 and 12 years old. A longitudinal psychometric model was fitted to the data and the fit of transmission and common factor models were evaluated for each variance component. WB showed considerable stability throughout childhood, with correlation coefficients ranging from about .30 for the 9-year time interval to .65 for shorter time intervals. Individual differences in WB as observed by the mother and the father were found to be largely influenced by genetic effects at all four time points, in both boys (50–66%) and girls (38–64%). Shared environmental influences explained a small to modest proportion (0–24%) of the variance at all ages and were slightly more pronounced in girls. Non-shared environmental influences were of moderate importance to the variance and slightly increased with age, from 22–28% at age 3 to 35–41% at age 12 years. The stability of WB was largely explained by genetic effects, accounting for 74% of stability in boys and 65% in girls. Shared environmental effects explained 7% (boys) and 17% (girls) of the behavioral stability. Most shared environmental effects were common to both raters, suggesting little influence of rater bias in the assessment of WB. The shared environmental effects common to both raters were best described by a common factor model, indicating that these effects are stable and persistent throughout childhood. Non-shared environmental effects accounted for the remaining covariance over time

Analytical methods applied to diverse types of Brazilian propolis

Propolis is a bee product, composed mainly of plant resins and beeswax, therefore its chemical composition varies due to the geographic and plant origins of these resins, as well as the species of bee. Brazil is an important supplier of propolis on the world market and, although green colored propolis from the southeast is the most known and studied, several other types of propolis from Apis mellifera and native stingless bees (also called cerumen) can be found. Propolis is usually consumed as an extract, so the type of solvent and extractive procedures employed further affect its composition. Methods used for the extraction; analysis the percentage of resins, wax and insoluble material in crude propolis; determination of phenolic, flavonoid, amino acid and heavy metal contents are reviewed herein. Different chromatographic methods applied to the separation, identification and quantification of Brazilian propolis components and their relative strengths are discussed; as well as direct insertion mass spectrometry fingerprinting