# Distribution Of Larynx Cancer In Lancashire Biology Essay

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Does the pattern of larynx cancer appear to cluster, or is it completely random? Based on this simple map, can you see any evidence to suggest that cases of larynx cancer cluster around the incinerator?

Based on the map above in figure 1, the point pattern of the larynx cancer appear not completely random or is spatial distributed in the study area, there is visual evidence to suggest that there is some degree of larynx cancer clustering around the incinerator

Fig 2a, b and c display 3D of kernel intensities of larynx cancer at different angles

Question2: Do the maps display any area of particular high intensity? What is the intensity of cancer around the site of incinerator?

Obviously, the maps in figure 2, displays some areas with high intensity in yellow colour and the intensity is in non uniform distribution across the area. Therefore there is relatively high intensity around the site of incinerator due to cluster effect in the area.Moreover; the 3D rotation of kernel intensity maps (Fig 2a, b and c) provides different graphic view of intensities around the study area.

Gatrell, A. et al (1995) pointed out the used of Kernel estimation intensity to weighs events according to their distance from the point.

Figure 3: Nearest neighbor empirical distribution of Larynx cancer

Question3: Examine the form of the G(w). What does it tell you about the pattern of larynx cancers?

The G function is plotted against S in figure 3 shows that the event starts from (0, 0) and gradually moves over a distance of 200 Units. It shows rapid increase from over a distance of 575 - 900 units or there about, indicating that many of the nearest distances fall within this range. We can deduct that the pattern of the larynx cancer from the G function is based on clustering effect on the nearest neighbor distance. O' Silluvan 2003 stated "When events are clustered, G increases rapidly at a short distance and while spaced events increases at slow rate".

Fig 4: K function against CSR model

Question4: Look at the graph it shows the value of K for range of distances (Plotted along the X- axis) compared along the value of K for a CSR model - is there any evidence of clustering and if so at what distances?

From the graph shown above there is evidence of clustering at distances between the range of 500 to 3000 units because the K (d) plot is higher than the CSR model at this range of distance. This indicates that events (larynx) cancers in the pattern are much nearer under the CSR mode.

Fig 5: L (w) function against CSR model

Question5: Looking at the result -how do you compare with those obtained from a graph of K (d) you constructed above?

Comparing the result of from the graph shows that;

Both functions have some degree of nearly the same geographical extent

Both shows clustering of the larynx cancer

The intensity of clustering at different scales appears best in L function than in K Function

Both do not have significant clustering from their origins

Question6: What assumptions have you made about the distribution of population and what conclusions might we wrongly reach, regarding the role of the of the incinerator in causing cancer, if we do not account for this factor.

Well, my conclusions were;

The disease larynx cancer is associated to the incinerator

On the other hand, the geographical area under study show that, the population is unevenly distributed as such incinerator cannot be solely responsible for the larynx cancer as there is some cluster far away from the incinerator.

POINT PATTERN ANALYSIS OF THE LUNG CANCER

Fig 6 Point Pattern distribution of lung cancer in Lancashire

Question 1:

Based on the map above in figure 6 above, The point pattern of the lung cancer appear not completely random or is spatial distributed in the study area, there is visual evidence to suggest that there is some degree of lung cancer clustering near the incinerator

Fig 7 a, b and c; Point Pattern distribution of lung cancer in Lancashire

Theta=50Â° theta=90Â° theta=180Â°

Fig 8 display 3D of kernel intensities of lung cancer at different angles

Question2:

The kernel maps show in Fig 7a and b above indicate a non uniform point pattern distribution of lung cancer across the study area. Moreover; the 3D rotation of kernel intensity maps (Fig 7a, b and c) provides different graphic view of intensities around the study area. However the intensity of lung cancer appears to be relatively low around the site of incinerator.

Fig 8: G function against CSR model

Question 3

The graph of G function plotted against s above (Figure 8) revealed how cases of lung cancer are spaced within the study area. As we can see from the graph, its moves rapidly at 500 - 700 units, then slows at 700- 1000 units or there about. However it shows some degree of stabilization over a distance of 1000 - 3000 units. From the graph above it is obvious that lung cancer is not completely random in the study area or it has some degree of spacing.

Fig 9: K function against CSR model

Question 4:

A graph of K-function compared to CSR model against distance (s) as shown above (Figure 9) there is evidence of lung cancer clustering in some parts of the study area. From 100unit to greater 4000 units indicates a strong evidence of clustering. K curve (black) rises above the red with covering distance of almost 2800 Units or there about.

Fig 10: L function against CSR model

Question 5: The figure 10 above, indicate clustering of lung cancer in the study area. The result of the L function shows a deviation from the CRS hypothesis, as L strays above the CRS model with positive values throughout the observed distance and this shows reflection of clustering.

Question 6: The disease of lung cancer might not be associated to the incinerator; as fewer cases are found around the incinerator area. As such incinerator is not responsible for the lung cancer. However, the analysis show an unevenly distribution of high population over the study area. And this might contribute to it.

Question 7: from the result of statistical analysis how does the pattern of LUNG cancers compare to that of LARYNX cancers?

From the above analysis we can deduct the followings;

Both lungs and larynx cancers are not completely random

Both shows significant deviation from CRS model

G function shows large number of nearest neighbor in lung cancers and on the other hand larynx cancers show small numbers of nearest neighbor, although both have similar pattern across the study area

K function shows clustering in both with higher in lung cancers

Both lungs and larynx cancers shows some high degree of clustering in certain geographical area

Fig 11 Point pattern distribution of larynx and lung cancers in Lancashire

Question 8: Assuming the lung cancer is a good measure of the underlying population distribution; are there any parts of the study area which seems to have an excess of larynx cancers?

From the fig 11 above, we can clearly see the site near incinerator shows a relatively higher incidence of larynx cancers as when compare with lung cancers. But however the lung cancer is more intense in geographical area

Question 9: In not more than 500 words, describe the application of point-pattern analysis in a GIS application context other than medical/health/epidemiology.

''Point Pattern Analysis involves the ability to describe patterns of locations of point events and test whether there is a significant occurrence of clustering of points in a particular area'' Frances F. Burden (2003)

The relative importance of point-pattern analysis may not be over emphasized, research have shown that point-pattern analysis plays important role in health or epidemiology, forestry, urbanization, archeology, astronomy, criminology, natural disasters like earthquakes, among others.

The point pattern analysis is one of most common technique for analyzing spatial distribution, partly because of its simplification and partly because there are a lot of spatial data collected as points.

In this exercise, attempt will be made using point pattern analysis to investigate issues regarding to criminology analysis and this is implemented using nearest neighbor distance statistics.

(P. Rogerson and Y. Sun 2001), in their journal spatial monitoring of geographic patterns, it attempts to describe a new procedure for detecting changes over time in the spatial pattern of point's events. It combines both the cumulative sum method as well as the nearest neighbor. The method used results in the flying detection of deviations from expected patterns.

Similarly, Yongmei Lu and Xuwei Chen (2006) uses point pattern analysis on the false alarm of planar K- function when analyzing urban crime distributed along streets.. the pointed the importance of K function as the common method commonly used for general point pattern analysis as well as crime pattern study

The importance of spatial point analysis in criminology has led to invented different software's like Ned levine's CrimeStat version 3.3. CrimeStat lll, which is currently being used by many police departments as well as criminal justice and other researchers

Today government and intelligent units including CIA's, KGB, FBI's, police and other criminal justice uses point pattern analysis in order to detect criminals.

Therefore, point pattern analysis is very powerful tool in criminology and it cannot be separated with GIS because, GIS gives information on every event of a spatial data.

References:

CrimeStat III: A Spatial Statistics Program for the Analysis of Crime Incident Locations (Version 3.3) http://www.icpsr.umich.edu/icpsrweb/ICPSR/studies/2824

Frances F. Burden 2003.Point Pattern Analysis.GIS Resource Document 03-41 (GIS_RD_03-41)

http://www.pop.psu.edu/gia-core/pdfs/gis_rd_03-41.pdf

Gatrell, C.A. et al (1995). Spatial point analysis and its application in geographical epidemiology. Transactions of the Institute of british Geographers, New Series Vol. 21(1) p256 -247

O' Silluvan D and Unwin D.J. (2003). Geographic Information Analysis. New Jersey:

P. Rogersson and Y. Sun (2001). Spatial monitoring of geographic patterns: an application to crime analysis. Computers, Environment and Urban Systems. Vol. 25(6) p539 - 556

Yongmei Lu and Xuwei Chen (2006). On the false alarm of Planar K- function when analysis urban distributed along streets. Social science Research Vol. 36(20 p611 - 632

http://uregina.ca/piwowarj/geog409/Lab3.pdf