Everything is related to everything else, but near things are more related than distant things.

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1 SPATIAL ANALYSIS

2 DR. TRIS ERYANDO, MA Everything is related to everything else, but near things are more related than distant things. (attributed to Tobler)

3 WHAT IS SPATIAL DATA? 4 main types event data, spatially continuous data, zonal data, spatial interaction data Most frequently used in social sciences is zonal data Data aggregated to a set of areal units (counties, MSAs, census blocks, ZIP codes, watersheds, etc.) Variables measured over the set of units Examples: Census, REIS, County and City Databook, etc.

4 THEORETICAL REASONS FOR SPATIAL ANALYSIS It tells us something more about what we re studying Is there an unmeasured process that affects the phenomenon? Does this process manifest itself in space? Examples: interaction processes, diffusion, historical or ethnic legacy, programmatic effects

5 EXAMPLES OF RESEARCH USING SPATIAL DATA ANALYSIS (SDA) Epidemiology (environmental exposure research) Criminology (crime patterns) Education (neighborhood effects on attainment) Diffusion/adoption (technologies) Social movements (trade unions, demonstrations) Market analysis (housing and land price variation) Spillover effects (economic spillovers of universities) Regional studies (regional income variation & inequality) Demography (segregation patterns) Political science (election studies)

6 WHEN DO YOU NEED TO DO SDA? Is there a theoretical reason to suspect differences across space? Differences in phenomena (variable values) Differences in relationships between phenomena (covariances) Are you using data with spatial referent? If yes to both, it is a good idea to at least explore any potential spatial effects Exploration will tell you more about the subject you re studying

7 GOALS OF SDA To identify spatial effects and their causes To appropriately measure spatial effects To incorporate spatial effects into models To improve our knowledge of the process and how it occurs over space All of these goals require both theory and methods

8 EXPLORATORY SPATIAL DATA ANALYSIS Start with questions about your theory and data: Are there likely to be spatial processes at work (diffusion, interaction, etc.)? Do your data units match the process? (Messner et al. reading) Visually and statistically explore your data Run basic descriptive statistics Map variables Look for patterns, outliers Look for spatial effects (large-scale variation, localized clusters)

9 FIRE OCCURRENCE PREDICTION MODEL UTILIZATION IN THE DKI JAKARTA PROVINCE- INDONESIA

10 BACKGROUND DKI Jakarta, Indonesia, is one of the most common areas of fire disaster. From January 2013 to April 2014, there were 921 fire cases occurred. With 175 billion dollars of material loss. This means that on average, 58 times a fire occurs every month or two fires a day. Fire preparedness activities require information related to the location of fire Objective: to prevent and estimate the risk of fire, with a statistical approach to predictive models of fire.

11 METHODS This study is an observational study of Exploratory Spatial Data Analysis (ESDA). The analysis used a spatial autocorrelation and Local Indicators of Spatial Association (LISA) The data used are secondary data results in 2012/2013 fire incident reports conducted by the Fire and Disaster Management in Jakarta)

12 RESULTS AND DISCUSSION

13 EXPLORATORY SPATIAL DATA ANALYSIS (ESDA) This section describes the results of the spatial analysis of the spread of fires in Jakarta and some of the factors that influence the spread of spatial data From Table, the spatial analysis of the spread of fires in Jakarta, showed Morannya index value = 0.12 (p<0.05), in the range 0 and 1, it can be concluded that resulting autocorrelation is positive spatial autocorrelation. Positive autocorrelation indicates the location or nearby villages have similar values and the incidence of fires in Jakarta tends to cluster.

14 the value of its Moran s Index shows all the variable has a value of positive spatial autocorrelation. Indicates positive autocorrelation nearby location have similar values, \the incidence of fires in Jakarta is tends to cluster. While the value of P-value fires at night is not significant. That is means that there is no difference in the form of the spread of fires at night.

15 LISA CLUSTER MAP the LISA cluster map shows that the relationship between the time of the fire incident. For some variables showed that most areas are not significant, only a few places that show the value of the High-High (HH) and the Low- Low (LL), which means it is likely to have a positive spatial autocorrelation value and the Regional high-high also shows areas that have a high observation value surrounded by regions with high observed values.

16 CONCLUSION. Fire in Jakarta most often occur at night, there is a dense residential area and is the main cause of the electrical installation problems. During this travel time to the fire location is quite short with an average of about 5 minutes after notification, yet handling over 30 minutes. The analysis of the spatial spread of fires in Jakarta, showed the value of the Moran index was 0.12 (p <0.05), and the incidence of fires in Jakarta tend to cluster

17 STANDARD DEVIATIONAL MODEL (SDE) FOR THE ANALYSIS OF DENGUE FEVER CASE IN BANJAR CITY, CIAMIS DISTRICT

18 BACKGROUND Dengue Fever Disease is still regarded as an endemic disease in Banjar City. Information is still required to map dengue fever case distribution, mean center of case distribution, and the direction of dengue fever case dispersion in order to support the surveillance program in the relation to the vast area of the dengue fever disease control program. The objective of the research is to obtain information regarding the area of dengue fever disease distribution in Banjar City by utilizing the Standard Deviational Ellipse (SDE)

19 METHOD The research is an observational study with Explanatory Spatial Data Analysis (ESDA). ESDA method of calculating the spatial aspects of using spatial analysis tools that Geographic Information Systems (GIS)* Data analysis uses SDE model with the scope of the entire sub district area in Banjar City. The data analyzed is dengue fever case from the period of , as many as 315 cases

20 SDE (Standard Deviation Ellipse) derived from a bivariate distribution. There are two interesting points about the distribution of point location in space of two dimensions: (1) centralization (central tendency) and (2) the spread (dispersion). Central tendency and dispersion is the mean center refers to the spread of the mean center of the ellipse bounded

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23 RESULT AND DISCUSION In 2007 there were 14 cases of dengue incidence in Banjar, which spread over the northern part of Banjar. 14 cases are divided into two main groups are separated by a hills and rivers. The first group/kelompok (9 cases of DHF) is located further than the second group (5 cases of DHF).

24 In 2012 there were 44 cases of dengue incidence in Banjar, which are mostly in the central part of the west to the town of Banjar, and a small spread in the northern and eastern part of Banjar

25 CONCLUSION The SDE model can be used to discover dispersion patterns and directions of dengue fever cases, therefore dengue fever disease control program can be conducted based on local-specific information, in order to support health decision

26 SPATIAL ACCESSIBILITY MODEL OF HEALTH CARE FOR TB PATIENTS IN WEST JAVA AND PAPUA PROVINCES, INDONESIA

27 Tuberculosis is one of the major health problems in developing countries. According to analysis conducted by Riskesdas 2013, the two highest pulmonary tuberculosis are are West Java (0.7%) and Papua (0.6 %). The aim of this study is a model of spatial access to health cares diagnosis of Tuberculosis patients in West Java province and the province of Papua and determine the pattern of access to health cares for TB patients.

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29 RESULTS

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31 Based on the value of the index s Moran, access to health cares in the province of West Java showed a pattern that spreads and have the same characteristics at a nearby location. Meanwhile, in the province of Papua showing patterns clustered and there s no spatial interaction.

32 CONCLUSIONS Access to health care of patients the diagnosis is still very low, it is proved that the diagnosis of TB patients who do access health cares in the province of West Java, only 63.23%, while in Papua province is only 45.77%. There s a Differences in spatial model of access to health cares diagnosis of TB patient in the province of West Java and Papua. Access to health cares should be provided by the government so that people can easily get the health cares.

33 ACKNOWLEDGE THE HEAD OF RESEARCH AND DEVELOPMENT OF THE MINISTRY OF HEALTH, RESEARCH AND DEVELOPMENT LOKA CIAMIS, WEST JAVA. THE HEAD OF SECTION AND THE STAFFS WHO HAVE GIVEN THE SUPPORTING DATA TO CONDUCT THE RESEARCH. AND DRPM UI.