Category: GEOB 479 Reviews

The purpose of Fitterer et al‘s paper, “Predictive crime mapping”, was to spatially predict commercial and residential break and entries (B&Es) in Vancouver, Canada. This paper aims to present a pilot project to have an automated model implemented within a mobile GIS that provides continually updated predictive maps to assist patrol units in self-deployment decisions. The data used for the study consisted of commercial and residential B&E data (provided by the Vancouver Police Department), population data from the 2011 Census and LandScan (ambient population), road network and land use data, graffiti data as a proxy for ‘lawlessness’, and data on average property values and house types were also used.

Fitterer et al’s methods suggested that an exploratory analysis was done in examining the space-time patterns of Break and Enters between 2001-2012, and a density map of BNEs was used to examine spatial patterns and hot spots. Furthermore, Ratcliffe’s near-repeat calculator was used to measure the spatial and temporal distance between each residential and commercial crime event that occurred (Fitterer et al, 2014). Based on observations made in assessing B&Es within 500m, 850m and 1000m from the original event and from one to 30 days since the event, two predictive models were developed for B&E crimes. Model 1 (Residential and Commercial) was based on integrating crime data with additional data using a generalized linear logistic regression estimation, while Model 2 was based only on observed crime data. Model 2 also assessed variable importance using the Knox ratio statistic, and assessed variable correlations to ensure models were statistically robust (Fitterer et al, 2014). Model accuracy was also assessed by visually comparing between observed and predicted spatial patterns. For Model 1, eleven data sets were found to be statistically significant predictors for residential BNEs, and 14 for commercial B&Es. These included B&Es within 850m from the event within the last 24 hours, 48 hours and seventh day, proportion of historical crime by time and day, road density, property value and type, count of B&E crimes in each cell, ambient and census population, and graffiti rate (Fitterer et al, 2014).

The results of the exploratory analysis found patterns in the characteristics of BNEs, including a progressive decrease in annual trends in the frequency of B&Es for both residential and commercial property types. On an hourly basis, residential offences decreased between 1:00 – 6:00, when residents were most likely to be at home, and peaked at 8:00, 12:00, and 18:00. Commercial B&Es occurred most often during 3:00, 4:00, 5:00, 17:00, and 18:00, while offences decreased during daylight hours (6:00-16:00). Spatially, the authors found consistency in patterns between 2001 and 2012, with Downtown parts of Vancouver having the majority of residential B&Es, with sections of the Westend and area around the Downtown core (Strathcona, Kitsilano, Fairview, Mount Pleasant, Oakridge, and Marpole) suffering from high numbers of property crime. Moreover, spatial consistency was found in commercial B&Es, with clusters around the Downtown neighbourhoods and surrounding regions of Fairview and Mount Pleasant, and local hot spots in the southern Arbutus ridge and Marpole areas. Overall, the results indicated that within short spurts of time and nearby vicinity of a break and enter, there is a high possibility of another break and enter occurring as well.

The limitations of the model are indicated by Fitterer et al., which includes the relatively rare occurrence of B&Es, spatial autocorrelation, and the difficulty to predict crimes at fine spatial resolutions due to the importance of scale between 850 m to 500 m (Fitterer et al, 2014). The methods portion of the paper also lacked any thorough explanation, specifically about the minimization of model choice bias through the use of considerations like AICc and Bandwidth parameter, or any other kinds of model accuracy evaluation. Due to this, I decided to give this paper an 8/10.

Article: Predictive Crime Mapping by J. Fitterer, T.A. Nelson & F. Nathoo

http://www-tandfonline-com.ezproxy.library.ubc.ca/doi/full/10.1080/15614263.2014.972618?needAccess=true

The article I reviewed for this section is called “Analyzing spatial clustering and the spatiotemporal nature and trends of HIV/AIDS prevalence using GIS: the case of Malawi, 1994-2010” and the aim of the study was to:

1. Examine spatiotemporal trends in HIV prevalence from 1994 – 2010
2. For 2010, it identifies and maps the spatial variation or clustering of factors associated with HIV prevalence at the district level in Malawi

The methods used for this study required 4 main steps:

• • Obtaining HIV prevalence data and temporal trends at national, regional and urban/rural scales
  • Determining spatial dependence in HIV prevalence, spatial interpolation, and spatiotemporal trends
    • First, the HIV prevalence rates for the pregnant women were plotted (from 1995 to 2010) to provide a spatiotemporal perspective of the HIV epidemics at its various levels (national, regional, urban and rural).
    • Then, they used GIS tools to
      • Empirically test for spatial dependency in HIV prevalence nationally
      • Produce a continuous surface of HIV prevalence at 1 x 1 km spatial resolution for visualization and generation of prevalence estimates at district level for cluster/hotspot and regression analysis.
    • HIV spatial autocorrelation was also assessed using global Moran’s I statistic
      • Which is indicative in suggesting whether HIV clustering occurs in a hierarchical expansionary spread in different areas and districts.
    • The Inverse Distance Weighted spatial interpolation method was used for the years 1994, 1996, 1999, 2001, 2003, 2005, 2007, and 2010.
      • uses weights based only on distance between measured and unmeasured points
      • IDP assigns more influence to measured values nearest an unmeasured location than to measured values located farther away
  • Local spatiotemporal variation in HIV prevalence and cluster/’hotspot’ analysis
  • Regression analysis and indicative drivers of HIV prevalence for 2010

For the results, the analysis revealed that there was a wide spatial variation of HIV prevalence at a regional, rural/urban, district and sub-district levels. However, prevalence was spatially plateauing out within and across ‘sub-epidemics’ while declining significantly after 1999. Prevalence showed statistically significant spatial dependence nationally following initial (1995-1999) localized, patchy low/high patterns as the epidemic spread rapidly. Locally, HIV “hotspots” clustered among eleven southern districts/cities while a “coldspot” captured configurations of six central region districts. Preliminary multiple regression of 2010 HIV prevalence produced a model with four significant explanatory factors (adjusted R2 = 0.688): mean distance to main roads, mean travel time to nearest transport, percentage that had taken an HIV test ever, and percentage attaining a senior primary education. Spatial clustering linked some factors to particular subsets of high HIV-prevalence districts.

I rate this article a 8/10 due to the limitations of the study which was also referenced within the article. The small sample size of 19 sentinel ANCs is not optimal as well as the use of OLS regression model was just sufficient enough and left out the spatial lag model findings.

The article I wrote about entails using ecosystem service values to assess the ecological values of corresponding land-use types, essentially to evaluate the ecological effects of regional land-use change within the study area of Yongsheng County in north west Yunnan Province between the years 1988 – 1999.

The main objective that was noted by the author were to: “(1) identify land-use change processes in China’s southwest agricultural landscape within the past 11 years through analysis of the dynamics of landscape metrics; and (2) consider relative changes from the global mean economic value of the ecosystem service in order to evaluate the ecological effects of LUCC.” (Peng et al, 2006, 317).

Land use/land cover change (LUCC) not only results in changes to the landscape structure of the Earth’s surface, but also influences material cycles and energy flow in the landscape, and biodiversity and important ecological processes in a region. This makes it worthwhile to study the relationship between the LUCC and the eco-environment to understand potential changes in the regional ecological environment. Assessing the ecological quality of regional land-use types according to the proportional relationship of the ecosystem service is necessary as well as then evaluating the ecological value change due to LUCC.

This methodological approach and the procedures used was appropriate for the research question as it analyzed land-use change with transition matrix identifying important processes, change in landscape diversity, and change in spatial configuration of landscape elements, change in patch shape. The ecological effects of land-use change were explored through integrated ecological effects of land use change in the whole country and spatial differentiation of ecological effects of land-use change. These factors all contribute to the arguments established by Peng et al that land-use change is largely existent due to human factors and affect ecological structures of the land. Due to the complexity and large scale of the paper, it is often very hard to articulate the grand scheme of the project into a concise and easy to understand manner. This paper should be commended for its easiness to read and follow through each procedure. For this reason, I would give this paper a 9/10. The only criticism would be its absence of translation of data into maps which would perfectly illustrate the results of the findings.

Article: Ecological effects associated with land-use change in China’s southwest agricultural landscape. International Journal of Sustainable Development & World Ecology