موقع GIS for WE نظم المعلومات الجغرافية (GIS) وبرامج ArcGIS

Cluster and Outlier Analysis (Anselin Local Morans I) Tool

أداة التحليل العنقودي والانعزالي (آنسلين و مورانس)

ArcMap ArcGIS

How to use Cluster and Outlier Analysis (Anselin Local Morans I) Tool in Arc Toolbox??

Cluster and Outlier Analysis 

كيفية استخدام أداة التحليل العنقودي والانعزالي (آنسلين و مورانس) ؟؟

Path to access the toolمسار الوصول الى الأداة

:

Cluster and Outlier Analysis (Anselin Local Morans I) Tool, Mapping Clusters Toolset, Spatial Statistics Tools Toolbox

 

Cluster and Outlier Analysis (Anselin Local Morans I)

Given a set of weighted features, identifies statistically significant hot spots, cold spots, and spatial outliers using the Anselin Local Moran's I statistic.

بالنظر إلى مجموعة من الميزات الموزونة ، يحدد النقاط الساخنة ذات الدلالة الإحصائية ، والبقع الباردة ، والقيم المتطرفة المكانية باستخدام إحصائية Anselin Local Moran I.

1. Input Feature Class ادخل فئة المعلم

The feature class for which cluster and outlier analysis will be performed.

فئة المعلم التي سيتم إجراء تحليل الكتلة والتحليل الخارجي لها.

Input Field

The numeric field to be evaluated.

Output Feature Class

The output feature class to receive the results fields.

Conceptualization of Spatial Relationships

Specifies how spatial relationships among features are defined.

·      INVERSE_DISTANCE—Nearby neighboring features have a larger influence on the computations for a target feature than features that are far away.

·      INVERSE_DISTANCE_SQUARED—Same asINVERSE_DISTANCE except that the slope is sharper, so influence drops off more quickly, and only a target feature's closest neighbors will exert substantial influence on computations for that feature.

·      FIXED_DISTANCE_BAND—Each feature is analyzed within the context of neighboring features. Neighboring features inside the specified critical distance (Distance Band or Threshold Distance) receive a weight of one and exert influence on computations for the target feature. Neighboring features outside the critical distance receive a weight of zero and have no influence on a target feature's computations.

·      ZONE_OF_INDIFFERENCE—Features within the specified critical distance (Distance Band or Threshold Distance) of a target feature receive a weight of one and influence computations for that feature. Once the critical distance is exceeded, weights (and the influence a neighboring feature has on target feature computations) diminish with distance.

·      CONTIGUITY_EDGES_ONLY—Only neighboring polygon features that share a boundary or overlap will influence computations for the target polygon feature.

·      CONTIGUITY_EDGES_CORNERS—Polygon features that share a boundary, share a node, or overlap will influence computations for the target polygon feature.

·      GET_SPATIAL_WEIGHTS_FROM_FILE—Spatial relationships are defined by a specified spatial weights file. The path to the spatial weights file is specified by the Weights Matrix File parameter.

Distance Method

Specifies how distances are calculated from each feature to neighboring features.

·      EUCLIDEAN_DISTANCE—The straight-line distance between two points (as the crow flies)

·      MANHATTAN_DISTANCE—The distance between two points measured along axes at right angles (city block); calculated by summing the (absolute) difference between the x- and y-coordinates

Standardization

Row standardization is recommended whenever the distribution of your features is potentially biased due to sampling design or an imposed aggregation scheme.

·      NONE—No standardization of spatial weights is applied.

·      ROW—Spatial weights are standardized; each weight is divided by its row sum (the sum of the weights of all neighboring features).

Distance Band or Threshold Distance (optional)

Specifies a cutoff distance for Inverse Distance and Fixed Distance options. Features outside the specified cutoff for a target feature are ignored in analyses for that feature. However, for Zone of Indifference, the influence of features outside the given distance is reduced with distance, while those inside the distance threshold are equally considered. The distance value entered should match that of the output coordinate system.

For the Inverse Distance conceptualizations of spatial relationships, a value of 0 indicates that no threshold distance is applied; when this parameter is left blank, a default threshold value is computed and applied. This default value is the Euclidean distance that ensures every feature has at least one neighbor.

This parameter has no effect when Polygon Contiguity or Get Spatial Weights From File spatial conceptualizations are selected.

Weights Matrix File (optional)

The path to a file containing weights that define spatial, and potentially temporal, relationships among features.

Apply False Discovery Rate (FDR) Correction (optional)

Specifies whether statistical significance will be assessed with or without FDR correction.

·      Checked—Statistical significance will be based on the False Discovery Rate correction for a 95 percent confidence level.

·      Unchecked—Features with p-values less than 0.05 will appear in the COType field reflecting statistically significant clusters or outliers at a 95 percent confidence level. This is the default.

Number of Permutations (optional)

The number of random permutations for the calculation of pseudo p-values. The default number of permutations is 499. If you choose 0 permutations, the standard p-value is calculated.

·      0—Permutations are not used and a standard p-value is calculated.

·      99—With 99 permutations, the smallest possible pseudo p-value is 0.01 and all other pseudo p-values will be even multiples of this value.

·      199—With 199 permutations, the smallest possible pseudo p-value is 0.005 and all other possible pseudo p-values will be even multiples of this value.

·      499—With 499 permutations, the smallest possible pseudo p-value is 0.002 and all other pseudo p-values will be even multiples of this value.

·      999—With 999 permutations, the smallest possible pseudo p-value is 0.001 and all other pseudo p-values will be even multiples of this value.

·      9999—With 9999 permutations, the smallest possible pseudo p-value is 0.0001 and all other pseudo p-values will be even multiples of this value.

2. Input Field الحقل الجدول

The numeric field to be evaluated.

الحقل الرقمي المراد تقييمه.

Output Feature Class

The output feature class to receive the results fields.

Conceptualization of Spatial Relationships

Specifies how spatial relationships among features are defined.

·      INVERSE_DISTANCE—Nearby neighboring features have a larger influence on the computations for a target feature than features that are far away.

·      INVERSE_DISTANCE_SQUARED—Same asINVERSE_DISTANCE except that the slope is sharper, so influence drops off more quickly, and only a target feature's closest neighbors will exert substantial influence on computations for that feature.

·      FIXED_DISTANCE_BAND—Each feature is analyzed within the context of neighboring features. Neighboring features inside the specified critical distance (Distance Band or Threshold Distance) receive a weight of one and exert influence on computations for the target feature. Neighboring features outside the critical distance receive a weight of zero and have no influence on a target feature's computations.

·      ZONE_OF_INDIFFERENCE—Features within the specified critical distance (Distance Band or Threshold Distance) of a target feature receive a weight of one and influence computations for that feature. Once the critical distance is exceeded, weights (and the influence a neighboring feature has on target feature computations) diminish with distance.

·      CONTIGUITY_EDGES_ONLY—Only neighboring polygon features that share a boundary or overlap will influence computations for the target polygon feature.

·      CONTIGUITY_EDGES_CORNERS—Polygon features that share a boundary, share a node, or overlap will influence computations for the target polygon feature.

·      GET_SPATIAL_WEIGHTS_FROM_FILE—Spatial relationships are defined by a specified spatial weights file. The path to the spatial weights file is specified by the Weights Matrix File parameter.

Distance Method

Specifies how distances are calculated from each feature to neighboring features.

·      EUCLIDEAN_DISTANCE—The straight-line distance between two points (as the crow flies)

·      MANHATTAN_DISTANCE—The distance between two points measured along axes at right angles (city block); calculated by summing the (absolute) difference between the x- and y-coordinates

Standardization

Row standardization is recommended whenever the distribution of your features is potentially biased due to sampling design or an imposed aggregation scheme.

·      NONE—No standardization of spatial weights is applied.

·      ROW—Spatial weights are standardized; each weight is divided by its row sum (the sum of the weights of all neighboring features).

Distance Band or Threshold Distance (optional)

Specifies a cutoff distance for Inverse Distance and Fixed Distance options. Features outside the specified cutoff for a target feature are ignored in analyses for that feature. However, for Zone of Indifference, the influence of features outside the given distance is reduced with distance, while those inside the distance threshold are equally considered. The distance value entered should match that of the output coordinate system.

For the Inverse Distance conceptualizations of spatial relationships, a value of 0 indicates that no threshold distance is applied; when this parameter is left blank, a default threshold value is computed and applied. This default value is the Euclidean distance that ensures every feature has at least one neighbor.

This parameter has no effect when Polygon Contiguity or Get Spatial Weights From File spatial conceptualizations are selected.

Weights Matrix File (optional)

The path to a file containing weights that define spatial, and potentially temporal, relationships among features.

Apply False Discovery Rate (FDR) Correction (optional)

Specifies whether statistical significance will be assessed with or without FDR correction.

·      Checked—Statistical significance will be based on the False Discovery Rate correction for a 95 percent confidence level.

·      Unchecked—Features with p-values less than 0.05 will appear in the COType field reflecting statistically significant clusters or outliers at a 95 percent confidence level. This is the default.

Number of Permutations (optional)

The number of random permutations for the calculation of pseudo p-values. The default number of permutations is 499. If you choose 0 permutations, the standard p-value is calculated.

·      0—Permutations are not used and a standard p-value is calculated.

·      99—With 99 permutations, the smallest possible pseudo p-value is 0.01 and all other pseudo p-values will be even multiples of this value.

·      199—With 199 permutations, the smallest possible pseudo p-value is 0.005 and all other possible pseudo p-values will be even multiples of this value.

·      499—With 499 permutations, the smallest possible pseudo p-value is 0.002 and all other pseudo p-values will be even multiples of this value.

·      999—With 999 permutations, the smallest possible pseudo p-value is 0.001 and all other pseudo p-values will be even multiples of this value.

·      9999—With 9999 permutations, the smallest possible pseudo p-value is 0.0001 and all other pseudo p-values will be even multiples of this value.

3. Output Feature Class فئة معلم الاخراج

The output feature class to receive the results fields.

فئة معلم الإخراج لتلقي حقول النتائج.

Conceptualization of Spatial Relationships

Specifies how spatial relationships among features are defined.

·      INVERSE_DISTANCE—Nearby neighboring features have a larger influence on the computations for a target feature than features that are far away.

·      INVERSE_DISTANCE_SQUARED—Same asINVERSE_DISTANCE except that the slope is sharper, so influence drops off more quickly, and only a target feature's closest neighbors will exert substantial influence on computations for that feature.

·      FIXED_DISTANCE_BAND—Each feature is analyzed within the context of neighboring features. Neighboring features inside the specified critical distance (Distance Band or Threshold Distance) receive a weight of one and exert influence on computations for the target feature. Neighboring features outside the critical distance receive a weight of zero and have no influence on a target feature's computations.

·      ZONE_OF_INDIFFERENCE—Features within the specified critical distance (Distance Band or Threshold Distance) of a target feature receive a weight of one and influence computations for that feature. Once the critical distance is exceeded, weights (and the influence a neighboring feature has on target feature computations) diminish with distance.

·      CONTIGUITY_EDGES_ONLY—Only neighboring polygon features that share a boundary or overlap will influence computations for the target polygon feature.

·      CONTIGUITY_EDGES_CORNERS—Polygon features that share a boundary, share a node, or overlap will influence computations for the target polygon feature.

·      GET_SPATIAL_WEIGHTS_FROM_FILE—Spatial relationships are defined by a specified spatial weights file. The path to the spatial weights file is specified by the Weights Matrix File parameter.

Distance Method

Specifies how distances are calculated from each feature to neighboring features.

·      EUCLIDEAN_DISTANCE—The straight-line distance between two points (as the crow flies)

·      MANHATTAN_DISTANCE—The distance between two points measured along axes at right angles (city block); calculated by summing the (absolute) difference between the x- and y-coordinates

Standardization

Row standardization is recommended whenever the distribution of your features is potentially biased due to sampling design or an imposed aggregation scheme.

·      NONE—No standardization of spatial weights is applied.

·      ROW—Spatial weights are standardized; each weight is divided by its row sum (the sum of the weights of all neighboring features).

Distance Band or Threshold Distance (optional)

Specifies a cutoff distance for Inverse Distance and Fixed Distance options. Features outside the specified cutoff for a target feature are ignored in analyses for that feature. However, for Zone of Indifference, the influence of features outside the given distance is reduced with distance, while those inside the distance threshold are equally considered. The distance value entered should match that of the output coordinate system.

For the Inverse Distance conceptualizations of spatial relationships, a value of 0 indicates that no threshold distance is applied; when this parameter is left blank, a default threshold value is computed and applied. This default value is the Euclidean distance that ensures every feature has at least one neighbor.

This parameter has no effect when Polygon Contiguity or Get Spatial Weights From File spatial conceptualizations are selected.

Weights Matrix File (optional)

The path to a file containing weights that define spatial, and potentially temporal, relationships among features.

Apply False Discovery Rate (FDR) Correction (optional)

Specifies whether statistical significance will be assessed with or without FDR correction.

·      Checked—Statistical significance will be based on the False Discovery Rate correction for a 95 percent confidence level.

·      Unchecked—Features with p-values less than 0.05 will appear in the COType field reflecting statistically significant clusters or outliers at a 95 percent confidence level. This is the default.

Number of Permutations (optional)

The number of random permutations for the calculation of pseudo p-values. The default number of permutations is 499. If you choose 0 permutations, the standard p-value is calculated.

·      0—Permutations are not used and a standard p-value is calculated.

·      99—With 99 permutations, the smallest possible pseudo p-value is 0.01 and all other pseudo p-values will be even multiples of this value.

·      199—With 199 permutations, the smallest possible pseudo p-value is 0.005 and all other possible pseudo p-values will be even multiples of this value.

·      499—With 499 permutations, the smallest possible pseudo p-value is 0.002 and all other pseudo p-values will be even multiples of this value.

·      999—With 999 permutations, the smallest possible pseudo p-value is 0.001 and all other pseudo p-values will be even multiples of this value.

·      9999—With 9999 permutations, the smallest possible pseudo p-value is 0.0001 and all other pseudo p-values will be even multiples of this value.

4. Conceptualization of Spatial Relationships تصور العلاقات المكانية

Specifies how spatial relationships among features are defined.

· INVERSE_DISTANCE—Nearby neighboring features have a larger influence on the computations for a target feature than features that are far away.

· INVERSE_DISTANCE_SQUARED—Same asINVERSE_DISTANCE except that the slope is sharper, so influence drops off more quickly, and only a target feature's closest neighbors will exert substantial influence on computations for that feature.

· FIXED_DISTANCE_BAND—Each feature is analyzed within the context of neighboring features. Neighboring features inside the specified critical distance (Distance Band or Threshold Distance) receive a weight of one and exert influence on computations for the target feature. Neighboring features outside the critical distance receive a weight of zero and have no influence on a target feature's computations.

· ZONE_OF_INDIFFERENCE—Features within the specified critical distance (Distance Band or Threshold Distance) of a target feature receive a weight of one and influence computations for that feature. Once the critical distance is exceeded, weights (and the influence a neighboring feature has on target feature computations) diminish with distance.

· CONTIGUITY_EDGES_ONLY—Only neighboring polygon features that share a boundary or overlap will influence computations for the target polygon feature.

· CONTIGUITY_EDGES_CORNERS—Polygon features that share a boundary, share a node, or overlap will influence computations for the target polygon feature.

· GET_SPATIAL_WEIGHTS_FROM_FILE—Spatial relationships are defined by a specified spatial weights file. The path to the spatial weights file is specified by the Weights Matrix File parameter.

يحدد كيفية تعريف العلاقات المكانية بين المعالم.

• INVERSE_DISTANCE — المعالم المجاورة لها تأثير أكبر على العمليات الحسابية لميزة الهدف أكثر من العناصر البعيدة.

• INVERSE_DISTANCE_SQUARED — مماثل لـ INVERSE_DISTANCE فيما عدا أن المنحدر أكثر حدة ، لذلك يتلاشى التأثير بسرعة أكبر ، ولن يكون هناك تأثير جوهري على العمليات الحسابية لهذه الميزة سوى الأقرب للميزة المستهدفة.

• FIXED_DISTANCE_BAND — يتم تحليل كل ميزة في سياق الميزات المجاورة. تستقبل الميزات المجاورة داخل المسافة الحرجة المحددة (نطاق المسافة أو مسافة الحد) وزنًا واحدًا وتؤثر على العمليات الحسابية للميزة المستهدفة. تستقبل الميزات المجاورة خارج المسافة الحرجة وزنًا صفريًا وليس لها أي تأثير على حسابات السمة المستهدفة.

• ZONE_OF_INDIFFERENCE - الميزات الموجودة ضمن المسافة الحرجة المحددة (نطاق المسافة أو مسافة الحد) لميزة الهدف تتلقى وزنًا واحدًا وتؤثر على الحسابات الخاصة بهذه الميزة. بمجرد تجاوز المسافة الحرجة ، تتضاءل الأوزان (وتأثير الميزة المجاورة على حسابات الميزة المستهدفة) مع المسافة.

• CONTIGUITY_EDGES_ONLY - فقط معالم المضلع المجاورة التي تشترك في حد أو تداخل ستؤثر على الحسابات لميزة المضلع الهدف.

• CONTIGUITY_EDGES_CORNERS - ستؤثر المعالم المضلعة التي تشترك في الحدود أو تشترك في العقدة أو التداخل في العمليات الحسابية لميزة المضلع الهدف.

• GET_SPATIAL_WEIGHTS_FROM_FILE — يتم تعريف العلاقات المكانية بواسطة ملف أوزان مكانية محددة. يتم تحديد المسار إلى ملف الأوزان المكانية بواسطة معلمة Weights Matrix File.

5. Distance Method طريقة المسافة

Specifies how distances are calculated from each feature to neighboring features.

· EUCLIDEAN_DISTANCE—The straight-line distance between two points (as the crow flies)

· MANHATTAN_DISTANCE—The distance between two points measured along axes at right angles (city block); calculated by summing the (absolute) difference between the x- and y-coordinates

يحدد كيفية حساب المسافات من كل معلم إلى المعالم المجاورة.

• EUCLIDEAN_DISTANCE - مسافة الخط المستقيم بين نقطتين (أثناء تحليق الغراب)

• MANHATTAN_DISTANCE — المسافة بين نقطتين مقاسة على طول المحاور بزوايا قائمة (كتلة المدينة) ؛ محسوبة بجمع الفرق (المطلق) بين إحداثيات x و y

6. Standardization التوحيد

Row standardization is recommended whenever the distribution of your features is potentially biased due to sampling design or an imposed aggregation scheme.

· NONE—No standardization of spatial weights is applied.

· ROW—Spatial weights are standardized; each weight is divided by its row sum (the sum of the weights of all neighboring features).

يوصى بتوحيد الصف كلما كان توزيع الميزات الخاصة بك متحيزًا بسبب تصميم أخذ العينات أو مخطط التجميع المفروض.

• لا شيء - لا يتم تطبيق معايير الأوزان المكانية.

• ROW - تم توحيد الأوزان المكانية ؛ كل وزن مقسوم على مجموع صفه (مجموع أوزان جميع المعالم المجاورة).

7. Distance Band or Threshold Distance (optional) نطاق المسافة أو مسافة العتبة (اختياري)

Specifies a cutoff distance for Inverse Distance and Fixed Distance options. Features outside the specified cutoff for a target feature are ignored in analyses for that feature. However, for Zone of Indifference, the influence of features outside the given distance is reduced with distance, while those inside the distance threshold are equally considered. The distance value entered should match that of the output coordinate system.

For the Inverse Distance conceptualizations of spatial relationships, a value of 0 indicates that no threshold distance is applied; when this parameter is left blank, a default threshold value is computed and applied. This default value is the Euclidean distance that ensures every feature has at least one neighbor.

This parameter has no effect when Polygon Contiguity or Get Spatial Weights From File spatial conceptualizations are selected.

يحدد مسافة القطع لخيارات المسافة العكسية والمسافة الثابتة. يتم تجاهل الميزات الموجودة خارج الحد المحدد لميزة مستهدفة في تحليلات تلك الميزة. ومع ذلك ، بالنسبة إلى منطقة اللامبالاة ، يتم تقليل تأثير الميزات خارج المسافة المحددة مع المسافة ، في حين يتم النظر في تلك الموجودة داخل عتبة المسافة على قدم المساواة. يجب أن تتطابق قيمة المسافة التي تم إدخالها مع قيمة نظام إحداثيات الإخراج.

بالنسبة لتصورات المسافة العكسية للعلاقات المكانية ، تشير القيمة 0 إلى عدم تطبيق مسافة عتبة ؛ عندما تُترك هذه المعلمة فارغة ، يتم حساب قيمة العتبة الافتراضية وتطبيقها. هذه القيمة الافتراضية هي المسافة الإقليدية التي تضمن أن كل ميزة لها جار واحد على الأقل.

لا يكون لهذه المعلمة أي تأثير عند تحديد تصورات Polygon Contiguity أو الحصول على أوزان مكانية من تصورات مكانية للملف.

8. Weights Matrix File (optional) وزن ملف المصفوفة (اختياري)

The path to a file containing weights that define spatial, and potentially temporal, relationships among features.

المسار إلى ملف يحتوي على أوزان تحدد العلاقات المكانية ، وربما الزمنية ، بين المعالم.

9. Apply False Discovery Rate (FDR) Correction (optional) تطبيق تصحيح معدل الاكتشاف الخاطئ (اختياري)

Specifies whether statistical significance will be assessed with or without FDR correction.

· Checked—Statistical significance will be based on the False Discovery Rate correction for a 95 percent confidence level.

· Unchecked—Features with p-values less than 0.05 will appear in the COType field reflecting statistically significant clusters or outliers at a 95 percent confidence level. This is the default.

يحدد ما إذا كان سيتم تقييم الأهمية الإحصائية مع أو بدون تصحيح FDR.

• تم الفحص - ستعتمد الأهمية الإحصائية على تصحيح معدل الاكتشاف الخاطئ لمستوى ثقة بنسبة 95 بالمائة.

• غير محدد - ستظهر الميزات ذات القيم p أقل من 0.05 في حقل COType مما يعكس مجموعات أو قيم متطرفة ذات دلالة إحصائية بمستوى ثقة 95 بالمائة. هذا هو الافتراضي.

10. Number of Permutations (optional) عدد التباديل (اختياري)

The number of random permutations for the calculation of pseudo p-values. The default number of permutations is 499. If you choose 0 permutations, the standard p-value is calculated.

· 0—Permutations are not used and a standard p-value is calculated.

· 99—With 99 permutations, the smallest possible pseudo p-value is 0.01 and all other pseudo p-values will be even multiples of this value.

· 199—With 199 permutations, the smallest possible pseudo p-value is 0.005 and all other possible pseudo p-values will be even multiples of this value.

· 499—With 499 permutations, the smallest possible pseudo p-value is 0.002 and all other pseudo p-values will be even multiples of this value.

· 999—With 999 permutations, the smallest possible pseudo p-value is 0.001 and all other pseudo p-values will be even multiples of this value.

· 9999—With 9999 permutations, the smallest possible pseudo p-value is 0.0001 and all other pseudo p-values will be even multiples of this value. Number of Permutations (optional)

عدد التباديل العشوائي لحساب القيم الزائفة p. العدد الافتراضي للتباديل هو 499. إذا اخترت 0 تبديلات ، يتم حساب قيمة p القياسية.

• 0 - لا يتم استخدام التباديل ويتم احتساب قيمة p قياسية.

• 99 — مع 99 من التباديل ، فإن أصغر قيمة زائفة ممكنة هي 0.01 وستكون جميع قيم p الزائفة الأخرى مضاعفات هذه القيمة.

• 199 — مع 199 تبديلًا ، تكون أصغر قيمة p زائفة ممكنة هي 0.005 وستكون جميع القيم الزائفة الأخرى الممكنة مضاعفات هذه القيمة.

• 499 — مع 499 من التباديل ، فإن أصغر قيمة زائفة ممكنة هي 0.002 وجميع القيم الزائفة الأخرى ستكون مضاعفات هذه القيمة.

• 999 - مع 999 تبديلًا ، تكون أصغر قيمة p زائفة ممكنة هي 0.001 وستكون جميع قيم p الزائفة الأخرى مضاعفات هذه القيمة.

• 9999 — مع 9999 من التباديل ، فإن أصغر قيمة زائفة ممكنة هي 0.0001 وجميع القيم الزائفة الأخرى ستكون مضاعفات هذه القيمة.

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