Directional Space Time Analytics

Data Tactics has been working on a set of problems that require considered solutions. The following method compares distributions at two points in time, with a particular focus on

changes in the overall morphology of the distribution as well as mobility of individual observations within the distribution over

that same period of time and contextually accounting for neighborhood effects. These dynamics are illuminating and

communicate time and explicitly account for underlying spatial dimension (Wy). Based on the integration of a dynamic local space-time together with direction statistics these methods

provide insights on the role of spatial dependence and uncontrolled variance over time and space.

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Directional Space Time Analytics

This analysis demonstrates the utility of directional space time analytics on regional stability distribution dynamics. Drawing on recent advances in geovisualization [1], we suggest a spatially explicit view of mobility.Based on the integration of a dynamic local indicator of spatial association (LISA) together with directional statistics and mapped data points to each observation, this framework provides new insights on the role of spatial dependence in regional stability and change. These approaches have been illustrated with state level incomes in the U.S. (1969-2008), Gross Domestic Product (1960 - 2011) Failed State Index (2010 - 2012), and GMTI data (t0, t1).

[1] Murray, A. T., Liu, Y., Rey, S. J., and Anselin, L. (2010). Exploring movement object patterns.

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Directional Space Time Analytics

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Per Capita Gross Domestic Product

A measure of the total output of a country that takes the gross domestic product (GDP) and divides it by the number of people in the country. The per capita GDP is especially useful when comparing one country to another because it shows the relative performance of the countries. A rise in per capita GDP signals growth in the economy and tends to translate as an increase in productivity.

GDP is widely used by economists to gauge economic recession and recovery and an economy's general monetary ability to address externalities. It is not meant to measure externalities. It serves as a general metric for a nominal monetary standard of living and is not adjusted for costs of living within a region.

Gross Domestic Product

GDP = private consumption + gross investment + government spending + (exports − imports), or

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GDP per. CapitaTime Span: 1960 to 2011 (51 temporal bin(s), 1 year intervals): 2000 to 2011 (12 temporal bin(s), 1 year intervals);

Spatial Area: Global;

Original Sample: 202 obs;

Data processing: imputation, @{users}, URLs, thresholding and mean centering;

Pruned Sample: 145 observations;

Method: Directional Local Indicator of Spatial Autocorrelation (Moran’s I) with space-time classifications of High-high (Hh), high-High, Low-Low (LL), High Low (HL), Low-High (LH);

Spatial Weights: knn4;

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> describe(dlisa$yr2000)

> describe(dlisa$yr2011)

V. Name n mean sd median mad min max range skew kurtosis yr2000 145 5759 9534 1491 1831 87 46453 46366 2.12 3.72yr2011 145 13292 20621 4666 5841 231 114232 114001 2.46 6.54

Directional Space Time Analytics

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Directional Space Time Analytics

https://vimeo.com/69775085

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Directional Space Time Analytics

2000:2011 (12 temporal bin(s), 1 year intervals);

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Directional Space Time Analytics

What is wrong with Vermont[1]?

- Seemingly nothing!- Lies within head of approximately normal distribution - Not an outlier in a classical statistical sense - Vermont remains below the US average but is closing the gap.

[1] State Median Income

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State Median IncomeTime Span: 1969 to 2008 (40 temporal bin(s), 1 year intervals)

Spatial Area: Contiguous United States;

Original Sample: 48 obs;

Method: Directional Local Indicator of Spatial Autocorrelation (Moran’s I) with space-time classifications of High-high (Hh), high-High, Low-Low (LL), High Low (HL), Low-High (LH);

Spatial Weights: Rook Contiguity;

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Directional Space Time Analytics

1969:2008 (40 temporal bin(s), 1 year intervals)

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Directional Space Time Analytics

1969:2008 (40 temporal bin(s), 1 year intervals)

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Directional Space Time Analytics

1969:2008 (40 temporal bin(s), 1 year intervals)

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Directional Space Time Analytics

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Directional Space Time Analytics

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Directional Space Time Analytics

Monday, July 15, 13

Directional Space Time Analytics

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