Category Archives: Uncategorized

Some images have requires some radiometric processing in order to increase the contrast of the image and ensure that features in the image are distinguishable. This image shows an example of an image before any corrections. Notice the image above … Continue reading →

Geob 402assign1

IDRISI is a remote sensing software that specialize in raster analytical functionality, covering the full spectrum of GIS and remote sensing needs from database query, to spatial modeling, to image enhancement and classification. Using the create composite tool in IDRISI, we are … Continue reading →

Choropleth maps This is one of the most common forms of thematic maps, and they can be easily created using ArcGIS or any other GIS software. Using ArcGIS, i am able to join different tabular data sets (e.g. housing cost per census … Continue reading →

GIS analysis of the Agricultural Land Reserve (ALR) in Central Okanagan, British Columbia Abstract Agriculture in British Columbia (BC) currently contributes $11 billion to the regional economy, and that value is expected to increase to $14 billion by 2017. Agricultural … Continue reading →

Abstract Waste (i.e. paper, plastic, glass, metal, or food waste products) diversion has been a key focus for reducing the overall waste disposal as part of the Zero Waste Action Plan (2014). This requires efforts in both education and infrastructure. … Continue reading →

Environmental Impact Assessment of the proposed Garibaldi at Squamish project Memo for EIA of the proposed project: EIA Squamish (1)                         Fig 1. Protected areas that are not suitable for the … Continue reading →

Softwood Lumber export from British Columbia (B.C) I created a proportional symbol flow map to show the exports of softwood lumber from B.C to top ten destinations globally. I used a perceptual scaling method (Flannery) rather than absolute scaling, in order … Continue reading →

Housing affordability: A good indicator? Affordability of housing compares the actual housing cost to the income of individual household as a ratio, which is a much better representation of the actual affordability of housing in a region. This is because … Continue reading →

Quantitative Data Classification Classification of quantitative data is very important as different classification methods portray a very different picture to the audience (Fig 1). The type of classification for the data should be chosen based on the characteristics of the data set. … Continue reading →