GIS in Physical Planning

Gianni Gorgoglione Physical Planning GISN14 Exercise 5

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GIS in Physical Planning

GIS based road and school location east of Lund City Practical Exercise

Introduction Lund city, situated in the county of Skania in the south of Sweden, has a population of about 115000 people. Many students come to Lund to study since there are a lot of Universities since 1666. The demand on house, hence theirs cost, is drastically rising due to growth of population in the city. Thus, many families find cheaper houses on the eastern part of the region. The consequence is that Södra Sandby, Dalby, Staffanstorp and Eslöv are facing the increasing of the house´s demand. Thereby, the municipalities have decided to plan and construct a new road in order to help the traffic from the Eastern part to Lund and then to Malmö. Furthermore, Lund Municipality has decided to build new school for this area for pupils between 7 and 12 years. Thus, it is important to study the right location to satisfy the increasing school demand due to the higher number of families in this area. Besides, the planners consider as important factor the possibility for children to reach the school by biking or walking this with the prospective to avoid the need of busses.

Aim A) Planning of a road corridor passing through the areas just east of Lund

B) Localization of a school in the same area based on population distribution

Methods Part A) From the beginning, in order to accomplish the data organization, it was necessary to set the environment to geo-process the files for the study area in ArcMap. All the data have been converted in Raster and projected with RT90 according to the following details:

Projection Transverse mercator

Unit Meters

Spheroid Bessel 1841

Central Meridian 15.80827778 E

Reference Latitude 0

Scale Factor 1.0

False easing 1500 000

False northing 0.0

Before converting to raster, the features “BB_2c5i” and “BB_2c6i” were merged as well the NP_2c5i and NP_2c6i. These last were also digitized with the aim to create feasible polygons for the final analysis.

Gianni Gorgoglione Physical Planning GISN14 Exercise 5

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A particular operation was necessary to extract the quarry from Land Use shape file. The reclassification of the created rasters was the following step. In detail, the DEM raster was fixed to attain the right elevation about the sea level by multiplying the DEM value with 8.59375. Then, the slope in percent computation was executed with the tool of Spatial Analyst to get a slope raster with a grid size of 25. The values assigned to the slope percent was 1 for slope percent between 0-3 and 3 for those higher than 3. The next reclassified raster was the Soil type. This was reclassified by changing the value according to the following table:

Soil type Cost

Rock 7

Landfill 2

Glacifluvium 1

Clay 7

Morain 1

Clay-Morain 3

Organic soils 7

Sand 3

Silt 3

Water 10

The merged raster of BB_2c5i and BB_2c6i was given value 5. The Land use was reclassified by using the table as follows with the exclusion of the quarry with value 11 :

Value Class Cost

2 Small town, <200 inhabitants 9

3 Small town, > 200 inhabitants 10

5 Country houses/houses with open grounds 6

7 Road and railroad with surrounding areas 4

11 Area for mineral extraction 10

12 Waste area 7

14 Urban green area 5

15 Sports area 6

18 Golf course 2

19 Park- non urban area 5

30 Farm land 3

31 Fruit trees 7

32 Grassland 3

40 Deciduous forest 7

44 Coniferous forest 5

45 Coniferous forest 5

48 Mixed forest 5

54 Clearing 2

55 Young forest 2

72 Swamp 5

81 Water 10

The raster Historical (HI_2C5I) was reclassified by using a scale from 1 to 10 within the tool distance in Spatial analyst. The raster of the quarry Hardebarga was created by assigned 1 to the polygon and zero to the no-data values. At the end, the Natural Park raster was classified into two classes named Natural parks and Natural reserves. The result of this reclassification made possible the generation of Rasters to use as cost maps for all the different layers shown in the figure below:

Gianni Gorgoglione Physical Planning GISN14 Exercise 5

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The next step was to create a unique weighted raster from each different cost map with the aim to calculate the costs for the road construction across the area. The operation was computed in Raster Calculator by using the following formula and the weights in the table below and on the right there is the figure with the result of the combination: ("Lu_reclass"*0.25)+("Hardebarga"*0.20)+("Builtup_area_reclass"*0.15)+("Protected_rec"*0.10)+("Historical_rec"*0.10)+("Soil_type_rec"*0.15)+("Slope_rec"*0.15)

Raster data set Influence

Slope 0.05

Soil type 0.15

Land use 0.25

Built up areas (not included in LU)

0.15

Hardeberga Quarry 0.20

Historical Sites 0.10

National parks and nature reserves

0.10

Quarry Historic Slope Soil type

National Park

Land Use Houses

Gianni Gorgoglione Physical Planning GISN14 Exercise 5

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The given shape file with population distribution in the

study area was used to estimate the percentage of

pupils in this area as follows in the calculation sheet

below.

In conclusion, two shape files with start (X= 1341345, Y= 6175200) and destination (X= 1343100,Y= 6183900) points were created. With the start point layer and Weighted raster cost were used to calculate “Distance Cost” that gave the least coast from the start point and “Back link” necessary to compute the map of the least costs directions. Distance Cost and Back link together with Destination point layer was used to calculate the final road by using the arc tool “Cost path”.

Part B) Children estimation in Eastern Lund This part was analyzed by using statistical data from the Swedish National institute of Statistics. The population of children between 7 and 12 years in Lund Municipality was taken for the year 2013. The total population in Lund was 114291 and the total of children in the age between 7 and 12 years was 7344 obtained as follows in the table below:

Tot. Pop Eastern area Calculation Percent of children East Lund

Children in Eastern Lund

22682 ( 0,0643*22682) 1458

(1457/22682) 6,43

Tot. Pop. Lund Calculation Percent of children Lund

114291 (7344/114291) 6,43

Calculation of Gravity center Then, the gravity center was calculated by using the population estimated for each point. This was calculated for each point on the border of the polygon formed by the shape file “rand_pop” by multiplying the the percert of children of East Lund with each cell containing the original total population of Lund. The points necessary to compute the gravity centers were extracted from the mask created as polyline (See figure below)

Age 2013

7 years 1350

8 years 1249

9 years 1241

10 years 1209

11 years 1220

12 years 1075

tot 7344

Gianni Gorgoglione Physical Planning GISN14 Exercise 5

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To accomplish the calculation of the Gravity Center, all the coordinates from the extracted points on

the polyline were weighted by multiply them with the number of pupils for each cell (See figure

below).

km/h m/10 min

bycycle 10 1700

walking 5 830

Then, the Gravity Center was calculated by using the population estimated for each point. This was calculated for each point on the border of the polygon formed by the shape file “rand_pop” by multiplying the the percert of children of East Lund with each cell containing the original total population of Lund. The points necessary to compute the gravity centers were extracted from the mask created as polyline (See figure on the right).

Estimation of the meters by

going with bicycle and by foot.

Gianni Gorgoglione Physical Planning GISN14 Exercise 5

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Figure 1 New Road location on Eastern Lund Municipality

Results The figure 1 shows the result from the analysis of least cost distance path for the new road within the Eastern area of Lund Municipality in relation of every weighted factor. The figure 2 represents the gravity point where the school should be located and the distance from it by walking with a velocity of 5 km/h (yellow circle) or by biking with a velocity of 10 km/h (orange color circle).

Gianni Gorgoglione Physical Planning GISN14 Exercise 5

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Figure 2 School location

Discussion The identification of the least cost corridor through the Eastern area of Lund has been wisely

weighted according to the classified costs for the different features. In fact, the “Cost Path” tool

strives to identify those cells with lowest cost and at the same time tries to keep as good as possible

its relation to the distance between the start and destination points. The first question, here, is that if

we could avoid to use as criterion the shortest distance and try to maintain “pure” costs criteria with

the view to get the absolute possible least cost.

The Multi Criteria Evaluation (MCE) with weighed criteria is an acceptable method to help planners in

making decisions. Besides, the MCE looks work better in combination of GIS tools. But of course this

is just speculation and other methods could be proved and compared.

The school location part is a good representation of how to combine GIS and geo-statistical methods

that in this case the Centre Gravity was computed to obtain the best location for the school in

relationship with the number of pupils. The result shows clearly that where clusters of population are

consistent the center of gravity is located closer to these. Logically, this is what one could expect

from this analysis. If there is some kind of error in the positioning of the Center Gravity point, it might

depend from the estimation method used to get the number of children in the Eastern area of Lund.

In conclusion, the school location seems to be well distributed according to the dispersion of the

population.