Analysis of Implementation the Evaluation of Guidance and Counseling Program at Senior High Schools of Singkawang

Focus of this study are (1) describe and analyze the implementation of the guidance and counseling program, (2) find some factors inhibiting the implementation of the guidance and counseling program. This study uses qualitative methods; using interview data collecting technique, tested its validity through triangulation. Subjects in this study are all teachers of guidance and counseling in the Senior High School of Singkawang as many as 10 people as well as principals and supervisors as the informants with the total of 11 people. Results (1) the implementation of evaluation of guidance and counseling program by the teachers still has many weaknesses on each phase of the evaluation, such as not understanding the evaluation models of the guidance and counseling program, how to apply them, and monitoring process that is not done in deeply and in detail, (2) Some factors inhibiting the implementation of the evaluation of guidance and counseling program are lack of knowledge and understanding of the evaluation of guidance and counseling program in the schools, lack of interest in developing professional competencies, and lack of guidance to the teachers in implementing the guidance and counseling evaluation program

The year when a given pixel within each urban environment exceeded the pre-defined DN value.

<p>Dark grey pixels represent existing urban areas prior to 1992, while light grey indicates areas with no sufficient light sources in 2013.</p

Bright lights, big city: Causal effects of population and GDP on urban brightness - Fig 5

<p>Causal interactions among NTL (nighttime lights), POP (population size), and GDP (Gross Domestic Product) of a) all cities, b) established cities, and c) dynamic cities. A solid line represents a statistically significant causal relationship, while a dotted line indicates no significant causality. An arrow head indicates the direction of causal relationship, and a double-headed arrow represents a bi-directional causal relationship.</p

Bright lights, big city: Causal effects of population and GDP on urban brightness

<div><p>Cities are arguably both the cause, and answer, to societies’ current sustainability issues. Urbanization is the interplay between a city’s physical growth and its socio-economic development, both of which consume a substantial amount of energy and resources. Knowledge of the underlying driver(s) of urban expansion facilitates not only academic research but, more importantly, bridges the gap between science, policy drafting, and practical urban management. An increasing number of researchers are recognizing the benefits of innovative remotely sensed datasets, such as nighttime lights data (NTL), as a proxy to map urbanization and subsequently examine the driving socio-economic variables in cities. We further these approaches, by taking a trans-pacific view, and examine how an array of socio-economic ind0icators of 25 culturally and economically important urban hubs relate to long term patterns in NTL for the past 21 years. We undertake a classic econometric approach—panel causality tests which allow analysis of the causal relationships between NTL and socio-economic development across the region. The panel causality test results show a contrasting effect of population and gross domestic product (GDP) on NTL in fast, and slowly, changing cities. Information derived from this study quantitatively chronicles urban activities in the pan-Pacific region and potentially offers data for studies that spatially track local progress of sustainable urban development goals.</p></div

NTL change rate represented by Theil-Sen slope values showing the rate of change from 1992 to 2013.

<p>Water is colored as white. Cities were grouped based on their growth intensity. Left panel contains cities with fast and more dynamic urban growth, while the right panel includes cities with more stable and less development.</p

Spatial data, analysis approaches, and information needs for spatial ecosystem service assessments: a review

<div><p>Operational use of the ecosystem service (ES) concept in conservation and planning requires quantitative assessments based on accurate mapping of ESs. Our goal is to review spatial assessments of ESs, with an emphasis on the socioecological drivers of ESs, the spatial datasets commonly used to represent those drivers, and the methodological approaches used to spatially model ESs. We conclude that diverse strategies, integrating both spatial and aspatial data, have been used to map ES supply and human demand. Model parameters representing abiotic ecosystem properties can be supported by use of well-developed and widely available spatial datasets. Land-cover data, often manipulated or subject to modeling in a GIS, is the most common input for ES modeling; however, assessments are increasingly informed by a mechanistic understanding of the relationships between drivers and services. We suggest that ES assessments are potentially weakened by the simplifying assumptions often needed to translate between conceptual models and widely used spatial data. Adoption of quantitative spatial data that more directly represent ecosystem properties may improve parameterization of mechanistic ES models and increase confidence in ES assessments.</p></div

Annual distribution of burned areas (1985–2015) in Canada’s forested ecosystems.

<p>Insets show details of the fire events in three sub-areas (ordered west to east). Ecozone boundaries are also shown (as per <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0197218#pone.0197218.g001" target="_blank">Fig 1</a>). Map generated in ArcGIS 10.1 (<a href="http://www.esri.com/software/arcgis/arcgis-for-desktop" target="_blank">http://www.esri.com/software/arcgis/arcgis-for-desktop</a>). Ecozone boundaries available via creative commons.</p

Average burned area, Theil Sen Test results, and estimated fire return intervals.

<p>Slopes only displayed for significant trends (p < 0.05). Slope percentages are relative to average annual area burned.</p

Histograms of percent area burned for each fire regime area from 1985 to 2015.

<p>GSL, SC and SP (shaded red) were the only fire regime areas to display a significant trend in percent area burned from 1985 to 2015 using the Thiel Sen test (α = 0.05).</p

Burned area and number of burned patches, by patch size class.

<p>Burned area and number of burned patches, by patch size class.</p