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RESUMEN Objetivo. Utilizar los sistemas de información geográfica (SIG) como herramienta complementaria para caracterizar la ganadería bovina realizada en la región de la Orinoquia. Materiales y métodos. A través del uso de tecnologías espaciales se recopiló la información concerniente a la orientación ganadera, fisiografía, cobertura vegetal y catastro de la zona de estudio para su posterior análisis a través del software ACCESS de Microsoft. Resultados. En un alto porcentaje de los predios ganaderos ubicados en los cuatro departamentos de la Orinoquía (Casanare:72.7%, Meta:49.5%, Arauca:42% y Vichada:32%) predominan las coberturas de pastos, herbazales y vegetación secundaria, confirmando la expansión en la frontera agropecuaria que es promovida por la actividad ganadera en el país. Conclusiones. El uso de los SIG, permite realizar una mejor planificación y distribución eficiente de los recursos destinados a mejorar el funcionamiento de los sistemas de producción. Por ejemplo, en zonas donde la matriz de coberturas predominante son los pastizales y herbazales, las estrategias en pro de la sostenibilidad pueden enfocarse en la implementación de sistemas silvopastoriles, contrario a lo que pasaría en zonas donde la matriz de coberturas tenga un alto porcentaje de bosques naturales.
ABSTRACT Objective. Use Geographic Information Systems (GIS) as a complementary tool to characterize cattle farming in the Orinoquia region. Materials and methods. Through the use of space technologys, information concerning the livestock orientation, physiography, vegetation cover and land registry of the study zone was collected for further analysis over Microsoft ACCESS software. Results. In a high percentage of the cattle ranches located in the four departments (Casanare: 72.7%, Meta: 49.5%, Arauca: 42% and Vichada: 32%) the cover of pastures, grasslands and secondary vegetation predominates, confirming the expansion in the agricultural border that has had the cattle activity in the country. Conclusions. The use of complementary tools such as GIS allows for better planning and efficient distribution of resources to improve the functioning of production systems, for example, in zones where the predominant coverage matrix is grasslands, strategies in pro of sustainability can focus on the implementation of silvopastoral systems, contrary to what would happen in areas where the matrix has a high percentage of natural forests.
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Animals , Cattle , Geographic Information Systems , Data Analysis , Animal HusbandryABSTRACT
Objective To explore the spatial distribution of measles from 2013 through 2015 in Liaoning province,China and to provide references for measles control and prevention. Methods The GeoDa 1.4. 6 program was used to conduct exploratory spatial data analysis to identify the spatial distribution characteristics and pattern of measles in Liaoning province. Results The frequency analysis showed that the measles epidemic situation appeared to have significant positive skewing within 105 counties of Liaoning province in each year from 2013 through 2015. The global trend analysis indicated a balanced trend in 2013 and 2015,and that the high incidence measles areas were located in the eastern and northern provincial regions in 2014. The global Moran'sⅠwas 0.294 5,0.391 9,and 0.147 7,and general G values were 0.015 9,0.012 0,and 0.013 5,revealing a positive spatial autocorrelation and a high-high aggregation model for each year between 2013 and 2015. The local spatial autocorrelation analysis recognized 5 core areas and 25 hot-spot counties with a high incidence of the measles epidemic,mainly distributed in Shenyang,Fuxin,Tieling,Fushun,Benxi,Liaoyang,Panjin,and Huludao. Conclusion Measles cases were clustered geographically in Liaoning province from 2013 through 2015. Spatial epidemiology methods may offer insights for the epidemiologic study of measles.
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Objective To understand the spatial distribution of hepatitis C in Chongqing and its influencing factors.Methods The surveillance data of hepatitis C in 38 counties in Chongqing from January 2010 to December 2014 were collected,and spatial autocorrelation analysis and spatial regression analysis were conducted respectively by using software GeoDa 1.6.7.Results The reported incidence of hepatitis C in Chongqing ranged from 7.3/100 000 to 13.6/100 000 during 2010-2014,with the annual reported incidence of 10.3/100 000.The global Moran' s I values were 0.478,0.503,0.529,0.438,0.406 respectively (P＜0.05).The local spatial autocorrelation analysis indicated there were 6,4,7,5 and 6 areas with high incidences of hepatitis C in 2010,2011,2012,2013 and 2014 respectively.Spatial regression analysis revealed that the reported incidence of hepatitis C in Chongqing was associated with the urbanization rate (Z=2.126,P=0.033).Conclusions The spatial distribution of hepatitis C in Chongqing from 2010 to 2014 was highly clustered.The hot spot of hepatitis C were mainly in the core areas and extended areas with well-developed economy,however the cold spot were in southeastern ecological reserve area with less developed economy.Urbanization had a certain positive influence on the distribution of hepatitis C in Chongqing.
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Objective The purpose of this study was to explore the spatial clustering,specific clustering areas,as well as changing trend of clustering areas of hand-foot-mouth disease (HFMD).Methods Exploratory spatial data analysis (ESDA) was used to conduct spatial statistical analyses for the HFMD using 2008-2011 data at both provincial and county/district levels.Results The Global Moran' s I coefficients appeared to be 0.3336,0.6074,0.3372,0.4620 and 0.4367 for 2008-2011and for the combined 4 years,respectively.The corresponding P-values were 0.002,0.001,0.004,0.001 and 0.001 respectively,when using the Monte Carlo tests with all the P-values less than 0.05.Moran' s Ⅰ coefficients ranged between 0.3 and 0.7,showing the appearance of moderate or higher clustering nature.Based on the results from nationwide analyses on clustering areas at the county/district levels between 2008 and 2011 (Moran' s I=0.5198,P=0.001),it appeared a moderate clustering nature.When local autocorrelation analysis was applied at the provincial level,3 hot spot areas in Beijing,Tianjin and Shanghai cities in 2008；7 hot spot areas in Beijing,Tianjin,Hebei,Shanxi,Shanghai,Jiangsu and Shandong in 2009; four hot spot areas:Beijing,Tianjin,Guangdong and Guangxi; five hot spot areas:Fujian,Jiangxi,Hunan,Guangdong and Guangxi in 2011,were discovered.390 hot-spot counties/districts were found through local autocorrelation analyses using the three-year data of 2008 to 2010.Conclusion Spatial clustering nature of HFMD incidence between 2008 and 2011 in China appeared to be moderate or high,with the clustered areas a north to south shifting trend.However,further investigation was in need to address this changing trend.
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Based on data related to human brucellosis which was collected from the national notifiable infectious disease reporting system in the 6 provinces(Inner Mongolia,Shanxi,Heilongjiang,Shaanxi,Jilin and Liaoning)of north China from 2004 to 2007,at the county scale.Data would include age and gender standardized mortality ratios(SMRs)while ESDA was including histograms,box plots and box maps,global and local Moran' s I statistics,etc.The global Moran' s I values from 2004 to 2007 were 0.2581,0.4574,0.4457,0.4841,respectively and all with statistically significant differences.Most of local Moran' s I values were significant positive statistically.High-high counties were mainly in the northeast,most of which were pastoral areas,but the farming-pastoral areas and agricultural areas/town had an increasing trend over time.Low-low counties were mainly in the western and southern areas and most of which were agricultural areas/towns.Low-high counties appeared to be rare,mainly around the counties with high incidence,mainly belonged to agricultural areas/towns.The incidence rates of brucellosis in the six provinces of north China had a trend of increase from 2004 to 2007,namely spreading from east to west,from south to north,and from pastoral areas to farming-pastoral areas and agricultural areas/towns.ESDA could be used to develop effective measures for prevention and control of brucellosis.
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Objective To analyze the spatio-temporal process on 2009 influenza A (HlNl) pandemic in Changsha and the influencing factors during the diffusion process. Methods Data were from the following 5 sources, influenza A (HlNl) pandemic gathered in 2009, Geographic Information System (GIS) of Changsha, the broad range of theorems and techniques of hot spot analysis, spatio-temporal process analysis and Spearman correlation analysis. Results Hot spot areas appeared to be more in the economically developed areas, such as cities and townships. The cluster of spatial-temporal distribution of influenza A (HlNl) pandemic was most likely appearing in Liuyang city (RR=22.70,P<0.01). The secondary cluster would include districts as Yuelu (RR=6A9,P< 0.01) , Yuhua (RR=81.63, P<0.01). Xingsha township appeared as the center in the Changsha county (RR=2.90, P<0.01) while townships as Yutangping (RR=19.31, P<0.01) , Chengjiao (RR=73.14,P<0.01) and Longtian appeared as the center in the west of Ningxiang county (RR= 14.43,P<0.01) and Wushan as the center in the Wangcheng county (RR= 13.84,P<0.01). As time went on, the epidemic moved towards the eastern and more developed regions. Regarding factor analysis, population, the amount of students, geographic relationship and business activities etc. appeared to be the key elements influencing the transmission of influenza A (H1N1) pandemic. At the beginning of the epidemic, population density served as the main factor (r=0.477, P<0.05) but during the initial and fast growing stages, it was replaced by the size of students to serve as the important indicator (r=0.831, P<0.01; r=0.518, P<0.01). However, during the peak of the epidemics, the business activities played an important role (r=-0.676, P<0.01). Conclusion Groups under high risk and districts with high incidence rates were shifting, along with the temporal process of influenza A(H1N1) pandemic, suggesting that the protection measures need to be adjusted, according to the significance of influencing factors at different stages.