{"id":11,"date":"2020-02-01T01:04:16","date_gmt":"2020-02-01T08:04:16","guid":{"rendered":"https:\/\/blogs.ubc.ca\/researchingis\/?page_id=11"},"modified":"2020-04-30T02:43:31","modified_gmt":"2020-04-30T09:43:31","slug":"presentation-summary","status":"publish","type":"page","link":"https:\/\/blogs.ubc.ca\/researchingis\/presentation-summary\/","title":{"rendered":"Presentation Summary"},"content":{"rendered":"

Assignment 1: Review of Use of Spatial Pattern Analysis to Assess Forest Cover Changes in the Mediterranean Region of Turkey<\/em><\/p>\n

Purpose<\/p>\n

Looking at Ba\u015fkonu\u015f Forest Enterprise, which is in the Mediterranean city of Kahramanmara\u015f in Turkey, the authors aimed to determine and map the main forest types from 1992-2012, evaluate the amount and rates of forest cover changes (FCC) in this time period, and investigate the factors of FCC (Bozali, Sivrikaya & Akay, 2015, p. 366).<\/p>\n

Main Argument<\/p>\n

The authors focused their investigations on the idea that the main factors of FCC were afforestation, forest conservation, and rehabilitation processes of the General Directorate of Forestry (GDF), which manages 99% of Turkey\u2019s forests (Bozali, Sivrikaya & Akay, 2015, p. 366).<\/p>\n

Key Points<\/p>\n

Knowledge of FCC is greatly important for those making decisions in regards to Turkey\u2019s forests (Bozali, Sivrikaya & Akay, 2015, p. 365).\u00a0 Human activities can fragment the landscape via urbanization, agriculture, livestock grazing, tree removal, and more (Bozali, Sivrikaya & Akay, 2015, p. 365).\u00a0 This may result in major changes like biodiversity loss and habitat isolation (Bozali, Sivrikaya & Akay, 2015, p. 366).\u00a0 This is why it is extremely important to determine the effects of policies for managing forests, like the GDF\u2019s 2006 Forest Rehabilitation Action Plan and Forest Conversion Action Plan (Bozali, Sivrikaya & Akay, 2015, p. 366).<\/p>\n

Methods and Analyses<\/p>\n

Maps used in forest management from 1992 and 2012 were digitized by being scanned and saved as TIFF files (Bozali, Sivrikaya & Akay, 2015, p. 367).\u00a0 The attribute data included land use and cover, crown closure, and stage of development (Bozali, Sivrikaya & Akay, 2015, p. 366).\u00a0 The two maps were overlaid in order to determine spatial and temporal changes (Bozali, Sivrikaya & Akay, 2015, p. 367).<\/p>\n

FRAGSTATS analyses were performed (Bozali, Sivrikaya & Akay, 2015, p. 368) and a transition matrix was created (Bozali, Sivrikaya & Akay, 2015, p. 371).\u00a0 The authors used landscape metrics like class % of landscape (PL), class area (CA: sum of areas of all patches belonging to a certain class), number of patches (NP), largest patch index (LPI: % of the landscape in the largest patch), mean patch size (MPS: the average patch size in a certain class), patch density (PD: number of patches per 100 ha), patch size coefficient of variation (PSCV), and area-weighted mean shape index (AWMSI: the average perimeter:area ratio for a class, weighted by the size of its patches) (Bozali, Sivrikaya & Akay, 2015, p. 368).<\/p>\n

Alternatives<\/p>\n

The methods and procedures used to conduct the study do appear to be appropriate for the research questions.\u00a0 Field surveys of plots in Ba\u015fkonu\u015f Forest could have been performed to enhance the data at hand.\u00a0 However, given that this method would have been extremely time-consuming and resource-consuming, the authors may have been justified in only using GIS and maps.<\/p>\n

Supporting Evidence<\/p>\n

Forested areas gained 376 ha from 1992 to 2012 due to GDF afforestation activities, GDF forest protective measures, and population migration from rural to urban areas (Bozali, Sivrikaya & Akay, 2015, pp. 368-370).\u00a0 As well, GDF rehabilitation activities led to a 1974.5 ha increase in productive forest areas, a 1598.5 ha decrease in degraded forest areas, a 684.6 ha decrease in non-forest areas, and a 1490.7 ha increase in conifer forests (Bozali, Sivrikaya & Akay, 2015, p. 370).<\/p>\n

Argument Validity<\/p>\n

The authors implied that the 2006 Forest Rehabilitation Action Plan and Forest Conversion Action Plan were the cause of the changed landscape of Ba\u015fkonu\u015f Forest.\u00a0 However, they did not explore other possible factors like climate change or interventions by non-state actors like citizens.<\/p>\n

Rating<\/p>\n

The authors did not specify who owns the other 1% of Turkey\u2019s forests (Bozali, Sivrikaya & Akay, 2015, p. 366) and how this would factor into the study topic.\u00a0 The authors also did not mention how the people living in the region were affected by, or any responses they had to, the 2006 forest management strategies.\u00a0 Thus, I give the authors a score of 8\/10.<\/p>\n

Work Cited<\/p>\n

Bozali, N., Sivrikaya, F., & Akay, A. E. (2015). Use of spatial pattern analysis to assess forest cover changes in the Mediterranean region of Turkey. Journal of Forest Research, 20<\/em>(4), 365-374. doi:10.1007\/s10310-015- 0493-2<\/p>\n

Assignment 2: Review of Use of Spatial Pattern Analysis to Assess Forest Cover Changes in the Mediterranean Region of Turkey<\/em><\/p>\n

Purpose<\/p>\n

Focusing on endemic areas of the Qom province in central Iran, the authors evaluated the risk of cutaneous leishmaniasis (CL) and epidemiological characteristics of the disease from 2009 until 2013 (Abedi-Astaneh et al., 2016, p. 1).<\/p>\n

Main Argument<\/p>\n

The authors aimed to resolve the uncertainty around CL transmission by analyzing epidemiological patterns of CL infection (Abedi-Astaneh et al., 2016, p. 3).\u00a0 The authors focused on the idea that the epidemiology of CL had changed (Abedi-Astaneh et al., 2016, p. 9).<\/p>\n

Key Points<\/p>\n

CL is present in Iran in two forms: Anthroponotic (ACL) and Zoonotic (ZCL) (Abedi-Astaneh et al., 2016, p. 2).\u00a0 Two species of the Phlebotomus <\/em>sand fly are known to be the main vectors of ACL and ZCL (Abedi-Astaneh et al., 2016, p. 2).\u00a0 Reservoir hosts of ZCL include different genera of gerbils (Abedi-Astaneh et al., 2016, p. 2).\u00a0 Reservoir hosts of ACL on the other hand, include humans and dogs (Abedi-Astaneh et al., 2016, p. 2).\u00a0 The long incubation period of CL leads to difficulties in determining the spatial location of disease transmission (Abedi-Astaneh et al., 2016, p. 2).<\/p>\n

Methods and Analyses<\/p>\n

Data on infected patients from 2009 to 2013 were retrieved from the Qom Province Health Center (Abedi-Astaneh et al., 2016, p. 3).\u00a0 To determine correlation between CL infection and parameters like age, gender, residential area and infection year and month, the data were analyzed with SPSS and Poisson regression and chi-squared tests with a 95% confidence level (Abedi-Astaneh et al., 2016, p. 3).\u00a0 9 of 212 samples that tested positive for Leishmania <\/em>spp. were used for molecular detection and identified as L. major<\/em> by PCR-RLFP (Abedi-Astaneh et al., 2016, p. 1).\u00a0 ArcGIS 10.3 was used to store the data of patient cases of CL (Abedi-Astaneh et al., 2016, p. 5).<\/p>\n

CL incidence rates were generated through inverse distance weighting (IDW) interpolation (Abedi-Astaneh et al., 2016, p. 5).\u00a0 The authors used Moran\u2019s I index statistics to perform cluster analysis of CL cases (Abedi-Astaneh et al., 2016, p. 5).\u00a0 As well, Getis-Ord Gi* statistic was used for hot spot analysis (Abedi-Astaneh et al., 2016, p. 5).<\/p>\n

Alternatives<\/p>\n

The methods used and analyses conducted were appropriate for the study.\u00a0 Interviews or further surveys regarding patient lifestyle and family medical history could have been done to gather other possible variables for CL infection.\u00a0 However, as this would have been much more time-consuming and resource-consuming, the authors were likely justified in using the methods and analyses detailed in their paper.<\/p>\n

Supporting Evidence<\/p>\n

Hot spot clusters in Qom City, the capital of the province, showed disease hot spots in the northeast and southwest of the city (Abedi-Astaneh et al., 2016, p. 7).\u00a0 The northeastern areas of Qom City are near agricultural fields, while the southwest is the location of a newly built housing project (Abedi-Astaneh et al., 2016, p. 7).\u00a0 The z-scores of Moran\u2019s I index verify the disease clustering trends, with under 5% likelihood that this could occur by chance (Abedi-Astaneh et al., 2016, p. 8).\u00a0 The authors linked the CL hot spot patterns with the presence of L. major<\/em> in patient lesions, the fact that rats and gerbils are found in agricultural fields and the fact that Phlebotomus<\/em> sand flies are known vectors of ACL and ZCL (Abedi-Astaneh et al., 2016, p. 9).\u00a0 They concluded that the epidemiology of CL had changed (Abedi-Astaneh et al., 2016, p. 9).<\/p>\n

Argument Validity<\/p>\n

Since the 9 CL patients that tested positive for L. major<\/em> did not travel to specific parts of Qom City (Abedi-Astaneh et al., 2016, p. 13), it is plausible that the authors contention that the epidemiology of the disease had changed (Abedi-Astaneh et al., 2016, p. 9) is correct.\u00a0 However, it is also possible that other vectors or variables could have led to the patient infections.\u00a0 Further research in the subject should be done to confirm uncertainty around CL transmission.<\/p>\n

Rating<\/p>\n

The authors were specific and mentioned that they received permission from Ethical Committee, Research Deputy, Tehran University of Medical Sciences to conduct their study (Abedi-Astaneh et al., 2016, p. 5).\u00a0 However, the colour and symbol choice of Figure 6, showing CL incidence, is difficult to decipher (Abedi-Astaneh et al., 2016, p. 10).\u00a0 As well, the legend of Figure 8, showing hot spot clusters in Qom City could be more descriptive (Abedi-Astaneh et al., 2016, p. 12).\u00a0 Thus, I give this paper a rating of 8\/10.<\/p>\n

Work Cited<\/p>\n

Abedi-Astaneh, F., Hajjaran, H., Yaghoobi-Ershadi, M., Hanafi-Bojd, A., Mohebali, M., Shirzadi, M., . . . Mahmoudi, B. (2016). Risk mapping and situational analysis of cutaneous leishmaniasis in an endemic area of central Iran: A GIS-based survey.\u00a0<\/em>PLoS ONE,\u00a011<\/em>(8), e0161317. doi:10.1371\/journal.pone.0161317<\/p>\n

Assignment 3: Review of The Effects of ‘Alley-Gating’ in an English Town<\/em><\/p>\n

Purpose<\/p>\n

The authors evaluated the effectiveness of \u201calley-gating\u201d as a preventative measure against burglary in Oldham, northwest England (Haywood, Kautt & Whitaker, 2009, p. 361).<\/p>\n

Main Argument<\/p>\n

Since industrial towns in Britain commonly have rows of back-to-back terraced homes, the alleyways at the rear are vulnerable points of entry for burglars (Haywood, Kautt & Whitaker, 2009, p. 361).\u00a0 One method of crime reduction is installing gates across these alleyways, or \u201calley-gating\u201d, with only residents holding keys to the gates (Haywood, Kautt & Whitaker, 2009, p. 362).<\/p>\n

Key Points<\/p>\n

Criminal activity may be controlled by manipulating the environment (Haywood, Kautt & Whitaker, 2009, p. 362).\u00a0 For example, alleyways may be prone to crime due to easy access to buildings while at the same time obscuring the offender (Haywood, Kautt & Whitaker, 2009, p. 361).\u00a0 Perpetrators lower the risk of arrest by choosing to commit crimes in the cover of vegetation or in darkness (Haywood, Kautt & Whitaker, 2009, p. 362).\u00a0 Thus, alley-gating may discourage criminal activity and decrease the risk of burglary (Haywood, Kautt & Whitaker, 2009, p. 362).<\/p>\n

A single burglary is estimated to cost about \u00a32626, meaning that burglary costs England and Wales around \u00a31.8 billion each year (Haywood, Kautt & Whitaker, 2009, p. 363).\u00a0 Alley-gating is a fast and tangible method for burglary reduction and demonstrates that government funds are being well spent (Haywood, Kautt & Whitaker, 2009, p. 363).<\/p>\n

Methods and Analyses<\/p>\n

The authors looked at two years of burglary data (from August 31, 2005 to August 31, 2007) gained from the Greater Manchester Police (Haywood, Kautt & Whitaker, 2009, p. 364).\u00a0 The data contained information on domestic and non-domestic (including garden sheds and detached garages) burglaries (Haywood, Kautt & Whitaker, 2009, p. 365).\u00a0 Data about 766 gates were obtained from the Oldham Crime and Disorder Reduction Partnership (Haywood, Kautt & Whitaker, 2009, p. 365).<\/p>\n

Using GIS, the authors mapped crime and gate locations in order to perform analyses (Haywood, Kautt & Whitaker, 2009, p. 370).\u00a0 Ten buffers of 200 m were mapped at the gate locations and the weighted displacement quotients (WDQs) were calculated (Haywood, Kautt & Whitaker, 2009, p. 370).\u00a0 As well, interviews with residents of the gated homes were conducted (Haywood, Kautt & Whitaker, 2009, p. 372).<\/p>\n

Alternatives<\/p>\n

The methods and analyses used were appropriate for the purpose of the study.\u00a0 Further information about neighbourhood ethnic makeup, income levels, education levels, population or more could have been explored to look at other possible factors regarding gates and burglary.\u00a0 As this would have been extremely resource-consuming and time-consuming, the authors were likely justified in using those methods and procedures.<\/p>\n

Supporting Evidence<\/p>\n

Looking at 120 crimes, 74% occurred before gate installation and 26% occurred after (Haywood, Kautt & Whitaker, 2009, p. 367).\u00a0 Using a chi-squared test, the authors found the gates significantly decreased the chance of burglary to the homes they were built for (Haywood, Kautt & Whitaker, 2009, p. 368).\u00a0 WDQ values show that the gates have positive impacts on the surrounding areas (Haywood, Kautt & Whitaker, 2009, p. 372).\u00a0 The interviewed residents spoke of greater pride and community cohesion and favourable outlooks about the gates due to decreased crime (Haywood, Kautt & Whitaker, 2009, p. 372).<\/p>\n

Argument Validity<\/p>\n

Looking at 6193 burglary cases, the authors noted that the crime hot spots did not match the areas of gate installation (Haywood, Kautt & Whitaker, 2009, p. 365).\u00a0 So, the positive results of the gates may or may not hold true for the areas most affected by burglary.\u00a0 In fact, some areas with no burglaries prior to gate installation were found to have burglaries after installation (Haywood, Kautt & Whitaker, 2009, p. 372).\u00a0 As well, the study did not consider other possible factors that might influence crime, such as CCTV or security lighting (Haywood, Kautt & Whitaker, 2009, p. 378).<\/p>\n

Rating<\/p>\n

The authors helpfully suggested other solutions to lower crime, such as taller gates, self-locking spring-shut gates or increased patrols being used in conjunction with gates to prevent the climbing or bypassing of gates (Haywood, Kautt & Whitaker, 2009, p. 377).\u00a0 Figure 1, the only map in the article, showed the concentric gate buffers and could have been easier to interpret if it were not black and white (Haywood, Kautt & Whitaker, 2009, p. 370).\u00a0 For example, each 200 m of buffer could have been a different colour.\u00a0 Other maps could have been created, for example, showing the study area in relation to the whole country or showing burglary hot spots compared to gate locations.\u00a0 In conclusion, I give this paper a rating of 7\/10.<\/p>\n

Work Cited<\/p>\n

Haywood, J., Kautt, P., & Whitaker, A. (2009). The effects of ‘alley-gating’ in an English town. European Journal of Criminology, 6<\/em>(4), 361-381. doi:10.1177\/1477370809104687<\/p>\n","protected":false},"excerpt":{"rendered":"

Assignment 1: Review of Use of Spatial Pattern Analysis to Assess Forest Cover Changes in the Mediterranean Region of Turkey Purpose Looking at Ba\u015fkonu\u015f Forest Enterprise, which is in the Mediterranean city of Kahramanmara\u015f in Turkey, the authors aimed to determine and map the main forest types from 1992-2012, evaluate the amount and rates of […]<\/p>\n","protected":false},"author":29802,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"footnotes":""},"class_list":["post-11","page","type-page","status-publish","hentry"],"_links":{"self":[{"href":"https:\/\/blogs.ubc.ca\/researchingis\/wp-json\/wp\/v2\/pages\/11","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/blogs.ubc.ca\/researchingis\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/blogs.ubc.ca\/researchingis\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/blogs.ubc.ca\/researchingis\/wp-json\/wp\/v2\/users\/29802"}],"replies":[{"embeddable":true,"href":"https:\/\/blogs.ubc.ca\/researchingis\/wp-json\/wp\/v2\/comments?post=11"}],"version-history":[{"count":4,"href":"https:\/\/blogs.ubc.ca\/researchingis\/wp-json\/wp\/v2\/pages\/11\/revisions"}],"predecessor-version":[{"id":41,"href":"https:\/\/blogs.ubc.ca\/researchingis\/wp-json\/wp\/v2\/pages\/11\/revisions\/41"}],"wp:attachment":[{"href":"https:\/\/blogs.ubc.ca\/researchingis\/wp-json\/wp\/v2\/media?parent=11"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}