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Case12A Deriving Urban Population Density Functions by Monte Carlo Simulation

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Chapter 12 Monte Carlo Method and Applications in Urban Population and Traffic Simulations

Case Study 12A: Deriving Urban Population Density Functions in Uniform Area Unit in Chicago by Monte Carlo Simulation

This case study is based on the work reported in Wang, Liu & Xu (2019). The study area is the seven-county Chicago CMSA. Main data sources include the 2010 Census data and the 2010 Land Use Inventory data. We limit the simulation of population to the residential land use category so that simulated residents can better resemble the actual settlement pattern.
As stated in section 6.3 of Chapter 6 in the main book, it is desirable to have analysis areas of identical or similar area size in fitting urban density functions. Wang, Liu & Xu (2019) designed six area units with three distinctive shapes (square, triangle, and hexagon) and two scales to capture both zonal and scale effects. This case study uses only one shape (hexagon) in one size (1 km2) for illustration. A small number of areas on the edge of the study area are truncated and thus smaller.

The sub-folder ZoneEffect under the data folder Chicago includes:
1) feature residential.zip represents residential land use,
2) feature block.zip represents census blocks with field POP100 for population in 2010,
3) features tract.zip and blockgroup.zip represent census tracts and block groups, respectively, and
4) feature citycenter.zip is the city center.




URL: GitHub for Geospatial Analytics https://github.com/spatial-data-lab/knime-geospatial-extension
URL: GitHub for Workbook Issue Report https://github.com/UrbanGISer/CGA-KNIME-Workbook/tree/main

Create Random Points Grid 1KMX1KM Revisions from the KNIME Lab Manual,Computational Methods and GIS Applications in Social Science- Lab Manual:(1) When using the "Create Random Points" node, there is no need to specify the ID column. Ensure the "Replace" option is selected for geometry. (2) Exercise caution when utilizing the Parallel Chunk nodes with Create Random Points node as the latter can significantly consume computer memory.It’s essential to configure the parameters within the "Parallel Chunk Start" appropriately. A setting of 10 chunk counts has been proven to perform efficientlyon a laptop equipped with 64GB of memory.(3) The OBJECTID is updated to OBJECTID_left in several nodes due to the setting change in Created Random Points: In Node 25, OBJECTID_left isused for the Group column; in Node 28, it's employed for the Top (left) join column; in Node 29, OBJECTID_left is utilized for the Bottom (right) join column;and in Node 36, it serves as the Aggregation column.Node 1Node 2Node 3Node 4Node 5Node 7Node 6Node 8Node 9Node 11Node 12Node 13Node 15Node 14Node 16Node 17Node 18Node 19Node 20Node 21Node 22Node 23Node 24Node 25Node 26Node 27Node 28Node 29Node 30Node 31Node 32Node 33Node 34Node 36Node 35Node 37Node 38Node 39Node 10 GeoFile Reader GeoFile Reader Overlay Area GroupBy Math Formula Joiner Row Filter ParallelChunk Start Parallel Chunk End Multipart ToSinglepart GeoFile Reader Create Grid Column Filter Spatial Join GroupBy Geometry To Point GeoFile Reader Euclidean Distance Joiner Math Formula(Multi Column) Linear RegressionLearner Spatial Join GroupBy Geometry To Point Euclidean Distance Joiner Joiner Math Formula Math Formula(Multi Column) Linear RegressionLearner Math Formula GroupBy GroupBy Math Formula Joiner Number To String Bar Chart(JavaScript) Create RandomPoints Create Random Points Grid 1KMX1KM Revisions from the KNIME Lab Manual,Computational Methods and GIS Applications in Social Science- Lab Manual:(1) When using the "Create Random Points" node, there is no need to specify the ID column. Ensure the "Replace" option is selected for geometry. (2) Exercise caution when utilizing the Parallel Chunk nodes with Create Random Points node as the latter can significantly consume computer memory.It’s essential to configure the parameters within the "Parallel Chunk Start" appropriately. A setting of 10 chunk counts has been proven to perform efficientlyon a laptop equipped with 64GB of memory.(3) The OBJECTID is updated to OBJECTID_left in several nodes due to the setting change in Created Random Points: In Node 25, OBJECTID_left isused for the Group column; in Node 28, it's employed for the Top (left) join column; in Node 29, OBJECTID_left is utilized for the Bottom (right) join column;and in Node 36, it serves as the Aggregation column.Node 1Node 2Node 3Node 4Node 5Node 7Node 6Node 8Node 9Node 11Node 12Node 13Node 15Node 14Node 16Node 17Node 18Node 19Node 20Node 21Node 22Node 23Node 24Node 25Node 26Node 27Node 28Node 29Node 30Node 31Node 32Node 33Node 34Node 36Node 35Node 37Node 38Node 39Node 10 GeoFile Reader GeoFile Reader Overlay Area GroupBy Math Formula Joiner Row Filter ParallelChunk Start Parallel Chunk End Multipart ToSinglepart GeoFile Reader Create Grid Column Filter Spatial Join GroupBy Geometry To Point GeoFile Reader Euclidean Distance Joiner Math Formula(Multi Column) Linear RegressionLearner Spatial Join GroupBy Geometry To Point Euclidean Distance Joiner Joiner Math Formula Math Formula(Multi Column) Linear RegressionLearner Math Formula GroupBy GroupBy Math Formula Joiner Number To String Bar Chart(JavaScript) Create RandomPoints

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