PROJECT 2 : LAND USE CHANGE ANALYSIS

Land use change analysis in GIS is a valuable application that useful to study and understand the transformations occurring in the land cover and land use patterns over a specific period of time. It involves comparing and analyzing different land use/land cover maps or data sets from different time periods to identify changes, quantify their extent, and gain insights into the underlying processes.

• The below maps are the land use change maps for the year 1990, 2000, 2010. The temporal of 10 years gap is suitable to analyze and to identify major shifts in land use.

• The above maps represents the land usage of an area from the year 1990 until 2010. The area was covered majorly by forest in the year 1990 compared to year 2010 where the area has undergo several new development which consist of more residential area, mixed horticulture, orchards and rubber plantations. 
• The graphs below shows the changes in hectares of the land use in the particular area from year 1990 to 2000.

• The graph above shows the area changes from year 1990 to 2000.

• The graph above shows the area changes from year 2000 to 2010.

• The graph above shows the area changes from year 1990 to 2010.

The data from the graphs were obtained from QGIS analysis by using Union and Dissolve tools.

Land use change from year 1990 to 2000	AREACHANGE In hectares
Schrub to Residential Area	5.65
Residential Area to Residential Area	102.71
Orchard to Orchard	953.69
Schrub to Schrub	338.84
Forest to Forest	5508.64
Forest to Mixed Horticulture	120.27
Forest to Schrub	46.08
Schrub to Forest	45.89
Forest to Residential Area	123.46
Forest to Rubber	41.83

Land use change from year 2000 to 2010	AREACHANGE In hectares
ORCHARD TO ORCHARD	889.254656
ORCHARD TO FOREST	64.43759267
SCHRUB TO SCHRUB	225.0932633
SCHRUB TO ORCHARD	90.36796454
MIXED HORTICULTURE TO MIXED HORTICULTURE	120.269131
SCHRUB TO FOREST	66.15606247
RESIDENTIAL AREA TO RESIDENTIAL AREA	231.8181624
FOREST TO MIXED HORTICULTURE	344.7052982
REMAINED AS FOREST	4892.612451
FOREST TO RUBBER	71.42046932
FOREST TO RESIDENTIAL AREA	226.5100319
RUBBER TO RUBBER	41.83280498
SCHRUB TO RESIDENTIAL AREA	22.58770408

Land use change from year 1990 to 2010	AREACHANGE In hectares
Forest to Schrub	42.774
Forest to Mixed horticulture	464.974
Forest to Residential area	366.556
Remained as Residential Area	102.711
Remained as Orchard	889.255
Schrub to Orchard	90.368
Orchard to Forest	64.438
Remained as Forest	4852.721
Schrub to Forest	86.765
Forest to Rubber	113.253
Remained as Schrub	201.603
Schrub to Residential area	11.648
SCHRUB to RESIDENTIAL AREA	22.58770408

The major changes in land use from year 1990 until 2010 is due to increase in population in the particular area. As the population grows, there is a need for housing, commercial spaces, and infrastructure like roads, schools, and hospitals. Forest areas are often cleared to make way for residential developments and urban expansion. Economic Growth and Job Opportunities: Increasing population usually corresponds with increased economic activities and demands for various products. Rubber plantations, mixed horticulture areas, and orchards offer economic opportunities for agriculture, which can generate employment and income for the local population. With a growing population, there is an increased demand for food production. Forest areas may be converted into agricultural land to cultivate crops and meet the rising food requirements. Forested areas may be chosen for conversion due to their relatively flat terrain, suitable soil conditions, and accessibility to transportation networks. Such factors make them attractive for residential and agricultural developments. Government policies and incentives can influence land-use decisions. In some cases, authorities may provide incentives or subsidies for the conversion of forests into rubber plantations, horticultural areas, or orchards, aiming to boost the local economy or support specific industries. Changing consumer preferences and market demands can also drive land-use changes. For example, if there is a high demand for rubber or specific horticultural products, forest areas may be converted to cater to those demands.

The Union and Dissolve Analysis in GIS is the major role player in analyzing the land use changes

In GIS (Geographic Information System), Union and Dissolve are two common spatial analysis operations used to manipulate and analyze spatial data.

• Union analysis combines two or more spatial datasets, typically polygon or line features, to create a new dataset that retains the attributes and geometry of the original features. The result is a dataset that combines the spatial extent of the input features and preserves the attributes from all the input datasets. For example, let's say you have two polygon layers: one representing states and another representing cities. By performing a union analysis on these layers, you would create a new layer where the polygons represent the combination of states and cities. The resulting layer would retain the attributes of both the state and city layers, allowing for analysis at a more detailed level. Union analysis is useful for tasks such as combining datasets for cartographic purposes, identifying overlapping or intersecting features, and analyzing the relationships between different datasets.

• Dissolve analysis simplifies spatial data by merging adjacent polygons or line features that share a common attribute value. It aggregates the features based on a specified attribute, creating new polygons or lines that represent the combined area or length of the dissolved features. For example, let's say you have a polygon layer representing individual land parcels and each parcel has an attribute indicating the owner's name. By performing a dissolve analysis based on the owner's name attribute, you would create new polygons where adjacent parcels owned by the same person are merged into larger contiguous areas. The resulting layer would have fewer polygons but retain the attribute information of the dissolved features. Dissolve analysis is useful for tasks such as generalizing detailed data, simplifying boundaries, removing redundancy, and creating new geographic units based on shared attributes.

Both Union and Dissolve analyses are fundamental tools in GIS for data manipulation, integration, and spatial analysis. They help to reveal patterns, relationships, and new insights by combining or simplifying spatial features based on their attributes.

More references and data can be found in REFERENCE and DATA REPOSITORY page in my blog.