SELCA Overview

SELCA is a QGIS plugin that analyzes the correlation between land cover changes and dependent variables such as population and GDRP, and provides probabilistic estimations of future land cover patterns.

Assign Class

Assigns descriptive class names to the class values in the land cover raster.

(PARAMETER)

LabelNameTypeDescription
Raster LayerINPUT[raster]Land cover raster to be reclassified

(OUTPUT)

LabelNameTypeDescription
Reclassified RasterOUTPUT[raster]Raster with new class labels

Land Cover Insight

Calculates land cover transition between classes land cover over time in the form of a transition matrix.

(PARAMETER)

LabelNameTypeDescription
Initial RasterINPUT1[raster]Initial raster
Final RasterINPUT2[raster]Final raster

(OUTPUT)

Produces output including statistical area charts of land cover, a land cover transition matrix (in area/km²), a heat map of the transition matrix, and descriptive explanatory text.

LabelNameTypeDescription
Matrix TableOUTPUT[CSV/Table]Transition matrix expressed in area and percentage units

Dependent Insight

Displays the relationship between land cover area and dependent variables, along with charts and visualizations of the land cover centroid shift. Users can also input variables such as GDRP, population, and others

(OUTPUT)

LabelNameTypeDescription
PlotCHART[PNG]Scatterplot visualization of variable relationships

Regression

Performs spatial regression between land cover classes and dependent variables using Common, Fixed, and Random Effect models, along with descriptive textual interpretation.

(OUTPUT)

LabelNameTypeDescription
Hasil RegresiCSV[Table]Estimation results and coefficient values
PlotPNG[Image]Regression result chart

Estimation

Performs area estimation of future land cover classes based on the transition matrix.

(PARAMETER)

LabelNameTypeDescription
Transition MatrixMATRIX[CSV]Initial Matrix
Number of YearsYEARS[Integer]Number of years ahead

(OUTPUT)

MProduces output including land cover class percentages, class-to-class transition matrices, future land cover class estimations, and comparative charts adjusted to the temporal interval.

LabelNameTypeDescription
Estimated AreaTABLE[CSV]Estimated area of future land cover classes

City Center Trend Analysis

Analysis of built-up center shift dari land cover multitemporal.

(PARAMETER)

LabelNameTypeDescription
Input Land Cover LayersINPUT[raster: multiple]Land cover raster layers from different time periods
Built-up ValueBUILD[number]A value that represents the built-up area in the land cover

(OUTPUT)

Generates the distribution of center of mass points and their directional shifts.

LabelNameTypeDescription
Centroid and Line LayerOUTPUT[vector: point+line]Layer of built-up center points and their directional shifts

Detecting Spatial Clusters and Outliers

Detects HH, LL, HL, and LH patterns based on variable values and their spatial relationships using Local Moran's I.

(PARAMETER)

LabelNameTypeDescription
Input LayerINPUT[vector: polygon]Input polygon layer
VariableVARIABLE[field: numeric]Numeric field value
ID FieldID_FIELD[field]Identifier field unit spaTial
MethodMETHOD[enum]Queen/Rook/KNN/Distance Band
KNN/ThresholdKNN_DIST[number]Spatial weight parameters

(OUTPUT)

Generates a layer based on the parameter calculation results, accompanied by descriptive text and a CSV table as output.

LabelNameTypeDescription
Result LayerOUTPUT[vector]Layer with HH, LL, HL, LH classification fields
CSVCSV_OUTPUT[file]CSV summary of values and classifications

Economic Sector Specialization Analysis

Measures economic sector specialization using the Location Quotient (LQ) method.

(PARAMETER)

LabelNameTypeDescription
Input LayerINPUT[vector]Input spatial layer
Sector ValueVARIABLEX[field]Specific Sector Value
Total ValueVARIABLEY[field]Total value of all sectors

(OUTPUT)

Generates a layer based on the parameter calculation results, accompanied by descriptive text and a CSV table as output.

LabelNameTypeDescription
Output LayerOUTPUT[vector]LQ field, classification, and interpretation
CSV OutputCSV_OUTPUT[file]Exported LQ result table

Global Spatial Pattern Analysis

Calculates Global Moran’s I to determine whether the spatial distribution of a variable is clustered, dispersed, or random.

(PARAMETER)

LabelNameTypeDescription
Input LayerINPUT[vector]Polygon or point layer
VariableVARIABLE[field]Numeric field value
MethodMETHOD[enum]Spatial weight method
K/ThresholdPARAM[number]KNN parameters or distance
ConfidenceCONFIDENCE[enum]99%, 95%, 90%

(OUTPUT)

Produces statistical figures as well as descriptive text and CSV tables as output.

LabelNameTypeDescription
PlotPLOT_OUTPUT[PNG]Scatterplot Moran’s I