Spatial Lag Model (SLM)

Maximum Likelihood Spatial Lag Model (공간시차모형, SLM)을 수행합니다.

Syntax

SpatialLagModel (SimpleFeatureCollection inputFeatures, String dependentVariable, String explanatoryVariables, SpatialConcept spatialConcept, DistanceMethod distanceMethod, StandardizationMethod standardization, Double searchDistance) : SimpleFeatureCollection, SpatialLagResult

Input Parameters

Identifier	Description	Type	Default	Required
inputFeatures	종속변수와 독립변수를 포함하고 있는 입력 레이어입니다.	SimpleFeatureCollection		✓
dependentVariable	종속변수값을 가진 숫자 필드입니다.	String		✓
explanatoryVariables	회귀 분석에 사용할 쉼표로 구분된 설명 변수 숫자 필드의 목록입니다.	String		✓
spatialDiagnotics	Lagrange multiplier, Moran’s I 등 공간 진단을 포함할 지 여부를 설정합니다.	Boolean		✓
spatialConcept	피처들 간에 공간 관계를 설정하는 방식을 선택합니다.	SpatialConcept	InverseDistance
distanceMethod	분석 대상 피처로부터 이웃 피처까지의 거리를 계산하는 방법을 설정합니다.	DistanceMethod	Euclidean
standardization	통계량 계산시 행 표준화 적용 여부를 설정합니다.	StandardizationMethod	None
searchDistance	역거리 혹은 고정 거리 옵션 선택 시 기준 값을 지정합니다.	Double	0.0

Process Outputs

Identifier	Description	Type	Default	Required
resFeatures	종속 변수 추정치와 잔차를 포함한 출력 레이어입니다.	SimpleFeatureCollection
report	분석 결과입니다.	SpatialLagResult		✓

Constraints

• Output은 XML로 반환한다.

Examples

a1_2000 필드를 종속변수로, a2_2000, a3_2000, a4_2000 필드를 설명변수로 분석한 결과는 다음의 XML 포맷으로 반환됩니다.

<?xml version="1.0" encoding="UTF-8"?>
<SpatialLagModel>
  <ModelName>Maximum Likelihood Spatial Lag</ModelName>
  <Dataset>seoul_series</Dataset>
  <DependentVariable>a1_2000</DependentVariable>
  <NumberOfObservations>25</NumberOfObservations>
  <NumberOfVariables>5</NumberOfVariables>
  <DegreesOfFreedom>20</DegreesOfFreedom>
  <MeanDependentVar>18229.716524000003</MeanDependentVar>
  <SdDependentVar>5222.973372203831</SdDependentVar>
  <LagCoefficient>-0.22828542473968091</LagCoefficient>
  <RSquared>0.2761455213123636</RSquared>
  <PseudoRSquared>0.27640866645133527</PseudoRSquared>
  <SpatialPseudoRSquared>0.24256289672449238</SpatialPseudoRSquared>
  <SigmaSquareML>1.895649856469702E7</SigmaSquareML>
  <SeOfRegression>4353.906127226105</SeOfRegression>
  <LogLikelihood>-245.08902967323908</LogLikelihood>
  <AkaikeCriterion>500.17805934647816</AkaikeCriterion>
  <SchwarzCriterion>506.27243847081917</SchwarzCriterion>
  <Summary>
    <Variable>
      <Variable>CONSTANT</Variable>
      <Coefficient>-95376.43702193913</Coefficient>
      <StandardError>41547.093017760526</StandardError>
      <ZStatistic>-2.2956223912264493</ZStatistic>
      <Probability>0.02169748136498197</Probability>
    </Variable>
    <Variable>
      <Variable>a2_2000</Variable>
      <Coefficient>1109.0234412138893</Coefficient>
      <StandardError>433.37762273421095</StandardError>
      <ZStatistic>2.559023316010134</ZStatistic>
      <Probability>0.010496670100781556</Probability>
    </Variable>
    <Variable>
      <Variable>a3_2000</Variable>
      <Coefficient>689.0420222891909</Coefficient>
      <StandardError>620.0027530256691</StandardError>
      <ZStatistic>1.1113531656538684</ZStatistic>
      <Probability>0.2664163810038307</Probability>
    </Variable>
    <Variable>
      <Variable>a4_2000</Variable>
      <Coefficient>62.425662528753676</Coefficient>
      <StandardError>519.9203359561636</StandardError>
      <ZStatistic>0.12006774540555191</ZStatistic>
      <Probability>0.9044294829669831</Probability>
    </Variable>
    <Variable>
      <Variable>W_a1_2000</Variable>
      <Coefficient>-0.22828542473968091</Coefficient>
      <StandardError>0.2815172692688138</StandardError>
      <ZStatistic>-0.8109109090629067</ZStatistic>
      <Probability>0.4174168425394321</Probability>
    </Variable>
  </Summary>
</SpatialLagModel>