Let\u27s Learn: All About Geography (Pre K - 3rd Grade) Student Copy

Student activity book. Inside you will find fun activities to help you learn about maps and geography. Keep an eye out for Sandy, the Chinook Salmon, for fun facts and helpful hints along the way! The teacher copy can be found here: https://archives.pdx.edu/ds/psu/27716https://pdxscholar.library.pdx.edu/geographyed_instructional/1008/thumbnail.jp

Spatial Analysis Of Human Capital Structures

An in-depth analysis of the occupational structure of the labour market in a spatial cross-section is an important theoretical and practical area of study necessary for the development of effective labour market policies and the education system

Spatial Analysis of Leptospirosis Disease in Bantul Regency YOGYAKARTA

Leptospirosis has still become a public health problem in the world, especially in developing countries which have tropical and subtropical climate such as in Indonesia. This research aims at investigating the spread and analyzing the cluster of Leptospirosis cases by using GIS. This research was conducted in 2015 using descriptive qualitative method. The total cases were 35 cased during May-Dec 2014 in Bantul Regency, Yogyakarta. The data consisted of secondary and primary data collected by using GPS. Univariate and spatial analysis were performed through SaTScan, QGIS desktop 2.4.0 and ArcGIS 1.1.0. The result shows that the distribution of Leptospirosis case in Bantul Regency is equally distributed in all districts with plain topography. The highest case occurs in May (12 cases). Clustering pattern is significant with p value= 0,001 with 11 cases in the cluster

Spatial Analysis and The Determinants of Mosquito Vector of Filariasis in The Endemic Areas of West Sumatera

BACKGROUND: Filariasis is an infectious disease caused by filarial worm (Wuchereriabancrofti, Brugiamalayi and Brugiatimori). It is trans-mitted by mosquito vector. Agam and West Pasaman are hyperendemic areas of filariasis in West Sumatra, with prevalence of 11.27 cases per 100,000 population, and 12.40 cases per 100,000 population, respectively. The environmental condition consists of mountain, plain, river, lake, plan-tation, and rice field. This study aimed to determine the risk factors asso-ciated with the incidence of filariasis and to implement the use of geogra-phic information system for mapping the vulnerability of area in West Sumatra. SUBJECT AND METHODS: This was case control study, conducted in Agam and West Pasaman districts, West Sumatera. A sample of 74 cases and 74 controls was selected for this study. Both study groups were matched by age and sex. Spatial and multivariate analyses were employed for data analysis. RESULTS: In Agam district, the type of vector was Culex (67.26%), Aedes (18.06%), Armigeres (14.19%), and Anopheles (0.48%). In West Pasaman district, the type of vector was Culex (70.25%), Aedes (20.25%), Armigeres (08.19%) and Anopheles (1.31%). In Agam and West Pasaman districts, the risk factors of filariasis included lack of knowledge (the strongest risk factor in Agam), absence of wire net, hanging out in the evening, use of mosquito bed net, absence of chemical insecticide, open clothes, presence of animal reservoir, living near plantation, paddy, river, marsh, shrub, and use of house ceiling (the strongest in West Pasaman). In Agam clustering of filariasis was found Subang, Muaro Putuih, Sungai Aur, Nagari Air Haji, Binjai, and the Crossing Valley. CONCLUSION: The strongest risk factor of filariasis in Agam is the lack of knowledge. The strongest risk factor of filariasis in West Pasaman is the use house ceiling. The most common type of filarial vector in Agam and West Pasaman is Culex. Keywords: Geographic Informatio

Multi Layer Spatial Analysis for Demersal Shrimp Fishery and Sst Warming in the Semarang Coastal Waters

Semarang coastal waters is part of coastal zone at the north coast of Java that is still has their characters for coastal demersal fishery. It was known for a long time before, that Semarang coastal water is a very good fishing ground especially for some valuable demersal species, such as white shrimp (Penaeus merguiensis), (Metapeneus.sp); flat fishes (3 species); Gastropods : Tiger snails (Babylonia.sp) and Bivalves : Anadara.sp. Some study that had been developed earlier in transforming from individual station data at coastal and seas, into visual-spatial layer in order to give more accurate spatial analysis of multiple parameters in the invisible coastal waters. This study present further development in the analysis of multi-layer spatial analysis. The samples of demersal coastal shrimp fishery and its closely related ecosystem parameters (depth; sediment; salinity) were taken randomly to represent the area of Semarang coastal zone. Field ecosystem and fishery samples data then processed using spatial method known as Kriging, and overlaid on a Landsat_TM satellite data. The study develops especially a multi layer of the field variables approach in order to analyze possible spatial multiple correlations between ecosystem parameter, such as type of bottom sediment, depth, and salinity to spatial distribution of shrimps spatial distribution as to represent demersal coastal fishery. This benthic fishery resources is regarded as the most vulnerable fishery due it\u27s sensitive character ie. sedentary and limited movement, is a good example to be used to monitor the impact of the environmental changes such global warming and climate change, such as seawater temperature anomaly in Semarang Coastal water (was found 1.39 ºC in March 1983) for the adaptation strategy in the future coastal resources management

Spatial Analysis in Veterans Cemetery Location

The National Cemetery Administration of the U.S. Department of Veterans Affairs has funded a relatively large number of new national and state veterans cemeteries in recent years to meet the burial needs of a growing number of aging veterans. This paper examines the history of this agency and the evolving role that spatial analysis has played in identifying appropriate locations for new cemeteries. It also examines some of the spatial assumptions used in cemetery planning and tests these assumptions in Virginia. Data from two Virginia state veterans cemeteries are examined to determine appropriate veterans cemetery service area boundaries. Finally, a location-allocation model is used to determine the best locations for a new veterans cemetery in Virginia.cemetery; veterans; location

International Comovement of Economic Fluctuations: A Spatial Analysis

We consider the comovement of economic volatility across multiple countries. Using spatial models with data from 187 countries over the period of 1960–2007, we find a strong spatial comovement of economic volatility. More interestingly, the effect of geographical proximity on economic volatility comovement is strongest during the period of international shocks (1973–86), but almost disappears over the globalization era (1987–2007). By way of contrast, the influence of trade relations in determining the comovement of economic volatility is significant over 1987–2007

The Spatial Analysis of Time Series

In this paper, we propose a method of analyzing time series, called the spatial analysis. The analysis consists mainly of the statistical inference on the distribution given by the expected local time, which we define to be the spatial distribution, of a given time series. The spatial distribution is introduced primarily for the analysis of nonstationary time series whose distributions change over time. However, it is well defined for both stationary and nonstationary time series, and reduces to the time invariant stationary distribution if the underlying time series is indeed stationary. The spatial analysis may therefore be regarded as an extension of the usual inference on the distribution of a stationary time series to accommodate for nonstationary time series. In fact, we show that the concept of the spatial distribution allows us to extend many notions and ideas built upon the presumption of stationarity and make them applicable also for the analysis of nonstationary data. Our approach is nonparametric, and imposes very mild conditions on the underlying time series. In particular, we allow for the observations generated from a wide class of stochastic processes with stationary and mixing increments, or general markov processes including virtually all diffusion models used in practice. For illustration, we provide some empirical applications of our methodology to various topics such as the risk management, distributional dominance and option pricing.