جغرافیا و برنامه ریزی محیطی، جلد ۲۳، شماره ۲، صفحات ۸۷-۱۰۰

عنوان فارسی پهنه بندی اقلیمی استان مرکزی با استفاده از تحلیل عاملی-خوشه ای
چکیده فارسی مقاله طبقه بندی اقلیمی نواحی جغرافیایی از گذشته‌های دور اذهان اقلیم‌شناسان را به خود مشغول کرده است، استفاده از چند پارامتر اقلیمی در روش‌های سنتی به تنهایی نمی‌تواند گویای واقعیت اقلیم نواحی باشد. بنابراین در سالیان اخیر محققان کوشیده‌اند با استفاده از غالب پارامترهای مؤثر بر اقلیم و روش‌های چند متغیره تصویری واقعی از اقلیم نواحی ارائه دهند. هدف این مقاله پهنه بندی اقلیمی استان مرکزی با روش تحلیل عاملی و خوشه‌ای است. در این روش‌ها غالب عناصر اقلیمی در تعیین نوع آب و هوای منطقه دخالت داده می‌شود. در این پژوهش با استفاده از روش تحلیل عاملی و خوشه‌ای پهنه بندی اقلیمی استان مرکزی صورت گرفت. برای بهبود نتایج طبقه بندی اقلیمی از آمار ایستگاه‌های مجاور تا نیم درجه جغرافیایی فاصله استفاده گردید. برای این امر یک ماتریس 21 در 29 شامل 21 ایستگاه سینوپتیک هوا‌شناسی و 29 متغیر اقلیمی تشکیل شد به علت تفاوت در مقیاس اندازه گیری متغیر‌ها از نمره استاندارد داده‌ها استفاده گردید. بررسی نتایج حاصل از تحلیل عاملی نشان داد که اقلیم منطقه متأثر از 6 مؤلفه غباری ـ برودتی، بارشی، ابرناکی ـ نمی‌، گرمایی، بارشی ـ سرمایشی و ابرناکی ـ تندری است. مؤلفه‌های یاد شده حدود 90 درصد رفتار آب و هوایی منطقه را تبیین کردند. تحلیل خوشه‌ای بر روی عوامل یاد شده وجود هفت ناحیه آب و هوایی را در منطقه نشان داد. این نواحی عبارتند از: ناحیه معتدل و نیمه مرطوب غباری، گرم و نیمه خشک، نیمه سرد و نیمه مرطوب غباری، معتدل و نیمه خشک غباری، معتدل نیمه خشک، سرد و نیمه خشک غباری و نیمه سرد مرطوب تندری. واژه‌های کلیدی: پهنه بندی آب و هوایی، تحلیل خوشه‌ای، تحلیل عاملی، استان مرکزی
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عنوان انگلیسی Climatic Regionalization of Markazi Province: An Application of Factor and Cluster Analysis
چکیده انگلیسی مقاله  Climatic Regionalization of Markazi Province: Application of Factor and Cluster Analysis      M. Khosravi. M. Armesh  Received: May 25, 2011 / Accepted: November 13, 2011, 25-28 P     Extended abstract  1- Introduction  The climatic classification from the distant past has attracted the attention of climatologists. In traditional methods one or more climatic elements considered for classification but these methods cannot indicates the reality of climatic regions. Therefore in the recent years researchers have tried using the dominant parameters affecting climate and multivariate methods have provided a real images from climatic regions. The aim of this study is climatic regionalization of Markazi province by utilizing 29 climatic parameters and use the factor and cluster analysis. Combined use of these parameters in the climatic classification can improves accuracy and shows a real aspect of province. Recognition of microclimates can help us to identify the strengths and weaknesses of regions climatic characteristics and useful for development planning proposes.   2- Methodology  The 29 climate variables from 21 synoptic stations from province and adjacent areas were used. By using the statistics of adjacent stations, accuracy and resolutions of factors and climatic zones were increased. The statistical data were normalized and also, due to different scales of data, the standard scores were used in analysis. The factor analysis and clustering method were applied for classification. After estimation of stations factor loading scores, by using of IDW method, 5*5km nodes were created, using these nodes instead stations in classification improved the accuracy of climatic classification. Eventually by calculation of factor scores in stations, a cluster analysis was applied. For interpolation purpose the kriging methods in GIS were used.     2-1- Factor analysis  The factor analysis as multivariate statistical methods can reduce the number of variables. The advantage of this method is that not only reduces the number of variables, but also keeps the variance of main data.  If the internal correlation between variables is much closer, the number of emerged factors is to be less.   2-2- Cluster analysis  In this method, the grouping of observations based on their distances, this means that observations have short distances classified in one cluster. The aim of clustering method is construction some group that the within group variance less than between group variance. The distance method usually applied for two or multi criteria clustering.  In this method, Euclidean geometry was used for distances measuring of members. According to Euclidean distance between spatial and temporal points, the distance matrices to be created that based on these matrix distances, determined the spatial and temporal cluster.   3- Argument  The factor analysis over variables was showed that the 6 components explained about 90% of region climatic behaviors. The factors with regards to weight of them over the variables are named. These principle components are Dust-coldness, precipitation, Cloudiness-humid, Thermal, precipitation- coldness and Cloudiness - Thunder . The dust-coldness factor has its maximum weights over Arak region. In south west of province, the precipitation factor were dominate and the cloudiness-humid factor active over the north of province. The thermal factor was affected over Arak and some of southeastern regions of province. Precipitation- coldness factor in Tafresh and north of province and finally Cloudiness- Thunder factor dominated over North West and Taftresh area. The cluster analysis over these 6 factors confirmed 7 climatic regions in Markazi province.  These regions are:  The temperate and semi-dust  Dusty and semi humid  Warm and semi arid  Dusty Semi cold and semi humid  Temperate and dusty semi arid  Semi arid Temperate  Cold and dusty semi arid  Semi cold and humid thunder.    4- Conclusion  In the studied area despite the homogenous synoptic systems Because of vitiate geographic factors such as elevation, topographic orientation latitude and etc, the role of synoptic systems are overshadowed. These caused numerous microclimates in the region. The results of factor analysis shown that climate of region affected by 6 components. These principle components are Dust-coldness, precipitation, Cloudiness-humid, Thermal, precipitation- coldness and Coldness – Thunder. These components explained about 90% of region climatic behavior . Cluster analysis shown 7 different climatic regions. 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نویسندگان مقاله محسن آرمش | آرمش
دانشگاه سیستان و بلوچستان
سازمان اصلی تایید شده: دانشگاه سیستان و بلوچستان (Sistan va baloochestan university)

محمود خسروی |
دانشگاه سیستان و بلوچستان
سازمان اصلی تایید شده: دانشگاه سیستان و بلوچستان (Sistan va baloochestan university)


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