农业工程学报
農業工程學報
농업공정학보
2015年
15期
272-278
,共7页
姬广兴%廖顺宝%岳艳琳%候鹏敏%杨旭
姬廣興%廖順寶%嶽豔琳%候鵬敏%楊旭
희엄흥%료순보%악염림%후붕민%양욱
粮食%误差修正%模型%样本尺度%分区方案%多元回归%产量%空间化
糧食%誤差脩正%模型%樣本呎度%分區方案%多元迴歸%產量%空間化
양식%오차수정%모형%양본척도%분구방안%다원회귀%산량%공간화
grain%error correction%models%sample scale%partitioning scheme%multiple variable regression%yield%spatialization
粮食产量数据空间化有助于粮食产量数据与其他自然、人文数据进行综合分析,但空间化过程中必然会产生误差。该文按照3种分区方案(全国不分区、全国分为7个区以及按省分区),选择3种尺度上(县级、地市级和公里网格)的总产及平均产量数据(即4种样本:县级粮食总产、县级平均粮食产量、地市级粮食总产、地市级平均粮食产量)分别为因变量,以对应的3种农田类型(水田、水浇地、旱地)面积数据为自变量,利用多元线性回归分析方法,得到15种空间化模型。采用两阶段误差分析方法,选取2个模型误差评价因子和5个空间化结果误差评价因子,对模型和空间化结果进行误差分析。结果表明:1)空间化过程中,模型精度与空间化结果的精度存在不一致性;2)对于采用同一样本的模型(常数项为0)而言,空间化结果精度随着分区方案的细化先提高再降低,而对于采用同一样本的模型(常数项非0)而言,空间化结果精度随着分区方案的细化而降低;3)在全国不分区和分为7个区2种情况下,空间化结果精度随着分析样本尺度的细化(从地市级到县级再到公里网格)先提高后降低。根据上述分析结果,最终以县级粮食总产为样本、常数项为0、全国分7个区建模的方案实现全国粮食产量数据空间化,并通过修正,得到2005年中国粮食产量公里网格分布图。该研究弥补了粮食产量空间化误差分析的不足,探寻了不同样本尺度和分区方案与空间化误差的关系,提高了空间化精度,同时对其他类型的社会经济统计数据空间化研究具有一定的参考价值。
糧食產量數據空間化有助于糧食產量數據與其他自然、人文數據進行綜閤分析,但空間化過程中必然會產生誤差。該文按照3種分區方案(全國不分區、全國分為7箇區以及按省分區),選擇3種呎度上(縣級、地市級和公裏網格)的總產及平均產量數據(即4種樣本:縣級糧食總產、縣級平均糧食產量、地市級糧食總產、地市級平均糧食產量)分彆為因變量,以對應的3種農田類型(水田、水澆地、旱地)麵積數據為自變量,利用多元線性迴歸分析方法,得到15種空間化模型。採用兩階段誤差分析方法,選取2箇模型誤差評價因子和5箇空間化結果誤差評價因子,對模型和空間化結果進行誤差分析。結果錶明:1)空間化過程中,模型精度與空間化結果的精度存在不一緻性;2)對于採用同一樣本的模型(常數項為0)而言,空間化結果精度隨著分區方案的細化先提高再降低,而對于採用同一樣本的模型(常數項非0)而言,空間化結果精度隨著分區方案的細化而降低;3)在全國不分區和分為7箇區2種情況下,空間化結果精度隨著分析樣本呎度的細化(從地市級到縣級再到公裏網格)先提高後降低。根據上述分析結果,最終以縣級糧食總產為樣本、常數項為0、全國分7箇區建模的方案實現全國糧食產量數據空間化,併通過脩正,得到2005年中國糧食產量公裏網格分佈圖。該研究瀰補瞭糧食產量空間化誤差分析的不足,探尋瞭不同樣本呎度和分區方案與空間化誤差的關繫,提高瞭空間化精度,同時對其他類型的社會經濟統計數據空間化研究具有一定的參攷價值。
양식산량수거공간화유조우양식산량수거여기타자연、인문수거진행종합분석,단공간화과정중필연회산생오차。해문안조3충분구방안(전국불분구、전국분위7개구이급안성분구),선택3충척도상(현급、지시급화공리망격)적총산급평균산량수거(즉4충양본:현급양식총산、현급평균양식산량、지시급양식총산、지시급평균양식산량)분별위인변량,이대응적3충농전류형(수전、수요지、한지)면적수거위자변량,이용다원선성회귀분석방법,득도15충공간화모형。채용량계단오차분석방법,선취2개모형오차평개인자화5개공간화결과오차평개인자,대모형화공간화결과진행오차분석。결과표명:1)공간화과정중,모형정도여공간화결과적정도존재불일치성;2)대우채용동일양본적모형(상수항위0)이언,공간화결과정도수착분구방안적세화선제고재강저,이대우채용동일양본적모형(상수항비0)이언,공간화결과정도수착분구방안적세화이강저;3)재전국불분구화분위7개구2충정황하,공간화결과정도수착분석양본척도적세화(종지시급도현급재도공리망격)선제고후강저。근거상술분석결과,최종이현급양식총산위양본、상수항위0、전국분7개구건모적방안실현전국양식산량수거공간화,병통과수정,득도2005년중국양식산량공리망격분포도。해연구미보료양식산량공간화오차분석적불족,탐심료불동양본척도화분구방안여공간화오차적관계,제고료공간화정도,동시대기타류형적사회경제통계수거공간화연구구유일정적삼고개치。
Spatialization of grain yield can contribute to comprehensive analysis of grain yield with other natural and cultural data. Grain production has a close relationship with the distribution of farmland. Therefore, information on spatial distribution of farmland is an important parameter for spatialization of grain yield, and the statistical analysis and modeling are the basic means to realize spatialization of grain yield. Spatialization of nationwide grain yield relates to sample scales and partitioning schemes. Different sample scales and partitioning schemes will inevitably lead to different errors of spatialization. In this paper, models considering farmland distribution and sample scales and partition schemes were proposed to estimate grain yield and its spatial distribution. The grain yield data were collected from 2005 Yellow Book of China. Data of paddy field, irrigated land, and dry land areas in each county or district were calculated. Four datasets of 3 scales were selected including total grain yields of counties, total grain yields of prefectures and their average grain yields. A total of 2321 county data and 349 prefecture-level data were obtained. 3 partitioning schemes (no partition of China, 7 regions of China, partitions of China by province) were considered. A total of 15 kinds of multiple variable linear models were constructed with area of different types of farmland as independent variables, grain yields as dependent variables. The results showed that: 1) Based on model fitness of grain yield and its spatialization results, optimal models could be selected since the model fitness suggested that the model without constant term based on prefecture-level data and 7 regions was best but the spatialization results indicated that the model without constant term based on county-level data and 7 regions was best; 2) For models without constant term, precision of spatialization results increased first and then decreased with scaling down of partitioning scheme; For models with constant term, precision of spatialization results decreased with scaling down of partitioning scheme; 3) In the 2 partitioning schemes (no partition of China and 7 regions of China), the precision of spatialization results increased first and then decreased with scaling down of samples from prefecture level to county level and 1 km by 1 km level; and 4) Compared with other models, in the case of county grain yields as samples, the model without constant term and 7 regions of China had the highest precison with coefficient of determination of 0.655. The spatialization results were modified with error by a proportional coefficient method, and the precision was improved to coefficient of determination of 0.968. This research made up for the deficiency of spatial error analysis of grain yield, explored the relationship between different sample scales and partitioning schemes and spatial error. Meanwhile, it also provided valuable information for other types of social and economic statistical data.