Risk and Vulnerability Assessment of Distrito Nacional, Santo Domingo, Dominican Republic

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1 Risk and Vulnerability Assessment of Distrito Nacional, Santo Domingo, Dominican Republic George McQueen Community & Regional Planning School of Architecture Latin American Studies Lozano Long Institute of Latin American Studies University of Texas at Austin CRP 386 Introduction to Geographic Information Systems December 14, 2009

2 EXECUTIVE SUMMARY Many government institutions in the Global South typically lack capacity to effectively use geospatial data for urban governance, including the ability to develop effective means to improve living conditions in informal settlements. The marginalized communities along the Ozama and Isablela Rivers in Distrio Nacional, Dominican Republic have been surveyed and studied regularly for the past 50 years. The communities and governing bodies are keenly aware of the environmental risks and social vulnerabilities of this area, but there appears to be little continuity between surveys and organizations. Surveys and maps become property of the sponsoring organization and each new effort to understand and address the environmental and social concerns requires the creation of new methodologies, new spatial references and the gathering of new data. Geographic Information Systems (GIS) can be a powerful tool to help improve communication and understanding among communities and government institutions. GIS can provide spatial and statistical information that can be communicated visually. This project will (1) review, categorize, document and prepare geospatial data provided by public institutions in the Dominican Republic, and (2) prepare supporting documentation of the data for use by public officials, technicians, and scholars working in Santo Domingo. Finally (3), this project will conduct a remote risk and vulnerability assessment of the Distrito Nacional, Santo Domingo, to demonstrate how GIS can be used to identify areas that are at risk of environmental hazards and vulnerable to a variety of social conditions. This will constitute an initial assessment, resulting in a recommendation for how geospatial data may be collected and used for further, more indepthe assessments of living conditions in the Distrito Nacional. This information may guide public works departments in prioritizing infrastructure improvement or municipalities in deciding where to site public services, such as community centers and health clinics. The assessment will also provide a model for future training of technicians and show the opportunities of GIS for the improvement of informal settlements in Santo Domingo and elsewhere in the Dominican Republic. 2

3 INTRODUCTION As the largest city and the capital of the Dominican Republic, Santo Domingo also has the highest population density. In a country of 8.5 million in 2002 (almost exactly 50 percent female, 50 percent male), 1.8 million live in the Santo Domingo Province, of which just over 900,000 live in the Distrito Nacional. Of the 48,442 square kilometers of the Dominican Republic, about 1/5th of the country's population lives in area of just 92 square kilometers of the Distrito Nacional. In other words, 21% of the population lives in.01% of the country. (ADN) While the population of the Dominican Republic continues to grow, there is also a shift in the countrywide population to the urban centers. (CONAU) Opportunities for jobs, education, services are all located in the cities; therefore there is a natural tendency for people living in rural communities to migrate towards these opportunities. As cities become more dense, the availability of these opportunities becomes more competitive, which is a disadvantaged to the most vulnerable community members. Housing opportunities also become more scarce, especially affordable housing, therefore people will settle where they can, even in areas that are hazardous, putting their well being at risk. (Navarro) As troubling as this is, this is a reality that affects most large cities, especially in Latin America. The Distrito Nacional is not an exception to the problems affecting large cities developing nations. Since the 1960s, the governing institutions have been trying to address the problems of of social and environmental inequality. Some efforts have been more successful than others, and the Distrito Nacional experienced its own form of urban renewal in the 60s and 70s. (Chantada) One area of continued concern and attention has been the land along the shores of the Ozama and Isabela rivers (see Figure 1). While the northern and eastern political boundaries of the Distrito Nacional are defined by these two rivers, so are many of the social and environmental conditions of the poor. These two large rivers converge just before reaching the Caribbean Sea. Sited at the confluence of three bodies of water, the Distrito Nacional has been a strategic location militarily and economically for centuries. However, this location also poses many unique environmental risks to the ever increasing population. (CONAU) In the mid 1990s, the municipality of the Distrito Nacional identified key areas of risk along the Isabela and Ozama Rivers with a project was called RESURE ( La Reestructuración Social, Urbana, y Ecológica de los Barrios Marginados que Rodean los Rios Ozama e Isabela; translated: The Social, Urban and Ecological Restructuring of the Marginalized Neighborhoods along the Ozama and Isablea Rivers ). This project proposed engaging the marginalized communities along the rivers to help improve the infrastructure and create autoconstrucción self help. (RESURE) The project also proposed the creation of a greenbelt along the river shore. This proposed relocating families found in areas of high risk and designating these areas as protected zones, which would allow for a contiguous green belt along the river. While intentions, actions and 3

4 results of RESURE (and RESURE II) are not in question, the methodology for the initial assessment remains unclear. Other similar efforts have been made to either improve conditions for or move people from these areas of highest risk, particularly in the neighborhoods of La Zurza and La Ciénega. These neighborhoods are among the poorest and located in the areas most susceptible to natural hazards in the Distrito Nacional.(RESURE) Other neighborhoods that are equally poor, with similar environmental hazards are Gualey, Puerto Isabela, Escanche Capotillo, Simón Bolivar, 24 de Abril, and Domingo Salvo. These neighborhoods represent a contiguous belt along the southwestern shores of the Isabel and Ozama Rivers. Another defining feature of these neighborhoods is the canyon, or the cañada. The cañada is the natural watershed drainage and is typically unsuitable for development, (Sletto) which is why the poorest people settle there albeit illegally because it is the only available land. Each of these neighborhood has a cañada, which is a continuous source of health and environmental risk. Following previous research and anticipating further investigation, this analysis will demonstrate how government officials can use census data and geographic information systems (GIS) shapefiles to anticipate and prevent certain social, economic and environmental crises. This analysis, however, does have very clear limitations. First, much of the data used for this project is over 10 years old and the conditions and infrastructure have likely changed. The accuracy of the original data is not known, therefore the accuracy of this assessment is limited. Second, much of the data had to be transferred from computer animated design (CAD) drawing files (.dwg) to GIS shapefiles (.shp). This creates additional opportunity for human error. Third, because this assessment was conducted remotely, and the author has not been to the study site, this is primarily a visual exercise in the possible uses of GIS. 4

5 PROBLEM STATEMENT Individual departments within governmental institutions gather and create information on the community and constituency they serve and represent. However, it is not uncommon for departments to be unable or unwilling to share this information. Cities in Latin America are not an exception, particularly the Distrito Nacional, Dominican Republic. Urban planners in Latin America have been traditionally educated as architects and use the tools of architects to document, represent and analyze space. Computer animated design is a common tools for Latin American planners/architects to create maps and plans. But as departmental budgets are constrained, so are the faculties to produce and use these maps and plans. In the Distrito Nacional, the marginalized communities along the Ozama and Isablela Rivers in Distrio Nacional, Dominican Republic have been surveyed and studied regularly for the past 50 years. The governing bodies are very aware of the environmental risks and social vulnerabilities of this area, but there appears to be little continuity between surveys and organizations. There also seems to be a limited capacity to represent these conditions. Geographic Information Systems (GIS) can be an effective tool that could help streamline the process of information creation and sharing. Instead of re-creating drawings, shapes can be shared and perfected and data can be stored in the attributes. GIS can help governmental institutions develop effective means to more quickly and accurately understand conditions, and create reports to represent this understanding. It is my hypothesis that the areas most socially vulnerable and at risk to environmental conditions can be identified through a cursory risk and vulnerability assessment, conducted remotely and with only spatial data. RESEARCH QUESTIONS 1) What are the principal environmental risks and vulnerability criteria in informal settlements in Santo Domingo? 2) Where are the locations of informal settlements in Santo Domingo? (Based on RESURE) 3) Where are the areas of greatest risk within informal settlements in the Distrito Nacional, Santo Domingo? (With available data, this will be based on the proximity to: the river, cañadas, floodplains, steel slopes and uneven soil) 4) What are the most vulnerable areas? ((With available data, this will be based on the distance from: education facilities, health clinics, police stations, markets and transit routes)) 5

6 METHODOLOGY Since this project is multi-layered, the methodology will be divided into the three parts mentioned in the executive summary: (1) understand and prepare data, (2) document the process of converting data from drawings to shapefiles, and (3) conduct a remote risk and vulnerability assessment Part 1 I acquired approximately six gigabytes of data on the Dominican Republic from Dr. Bjørn Sletto, professor of Community and Region Planning in the University of Texas at Austin School of Architecture. Government officials in the Distrito Nacional have shared spatial and census information with Dr.Sletto s because of his continued work in the Domincan Republic. I used ArcCatalog to open, preview and document the data. Most the data is defined and projected using "WGS 1984 UTM Zone 19N" as the projected coordinate system and "GCS WGS 1984" as the geographic coordinate system, but reviewing the shapefiles in ArcCatalog, some data will have to be defined and projected. Part 2 After understanding and organizing all the data, I realized most of the GIS data was of at the country-wide scale and I was hoping to work at the neighborhood scale. This was four levels of data removed from what I needed (The data is organized by Country, Province, Section, Municipality, Neighborhood, Block). Fortunately, there were many CAD drawing files that had neighborhood-level information. Unfortunately, it all had to be exported into GIS, defined, projected, georeferenced and resized to fit the proper scale. This process was documented in the Appendix and will hopefully be of future use. Part 3 I used the layers and shapes created in Part 2 to run a suitability assessment using GIS. Using ArcMap, ArcScene and other ArcGIS analytical tools (i.e. spatial analyst) I created a series of layouts that, if nothing more, give a visual description of the process of using GIS for a risk and vulnerability assessment (also represented in Figure 22). Adapting a methodology from the National Oceanic and Atmospheric Administration's (NOAA), assessed the risk and vulnerability of a specific site in the Distrito Nacional based social vulnerability and environmental risk. Given the limitation of the data, I defined social vulnerability as distance from public facilities including: education facilities, police stations, markets, public transportation and health centers. The environmental risk was defined by the proximity to environmental hazards, such as the rivers, cañadas, steep slopes, uneven surfaces and floodplains. The final product was these two assessments combined, which demonstrates the areas must vulnerable and at highest risk (see figures 17, 19 and 21 for representations of the final assessment. Figure 22 shows the process). 6

7 FINDINGS Series of Layouts: Fig. 1.1 Fig. 1.2 Fig. 1.3 Fig. 2 Fig. 3 Fig. 4 Fig. 5 Fig. 6 Fig. 7 Fig. 8 Fig. 9 Fig. 10 Fig. 11 Fig. 12 Fig. 13 Fig. 13 Fig. 14 Fig. 15 Fig. 16 Fig. 17 Fig. 18 Fig. 19 Fig. 20 Fig. 21 Context within the Caribbean Context within the Dominican Republic Context within Santo Domingo Percent of population in poverty in study area Focus area for assessment: Over 40% poverty Layers created from CAD files Environmental risk: proximity to river Environmental risk: proximity to a cañada Environmental risk: proximity to a floodplain Environmental risk: proximity to an uneven surface Environmental risk: proximity to a steep slope Areas most at risk because of environmental conditions Social vulnerability: distance from education facilities Social vulnerability: distance from health center Social vulnerability: distance from markets Social vulnerability: distance from police stations Social vulnerability: distance from public transportation Ares most vulnerable because of social conditions Both assessments combined: Areas most vulnerable and at risk Digital elevation model of study area Risk and vulnerability in relation to topography 3D image of study area, facing west 3D image of area with highest risk and most vulnerability Steps of a risk and vulnerability assessment using GIS 7

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30 ANALYSIS The purpose, implications and limitations of each layout will be discussed independently. Fig. 1.1 Context within the Caribbean This layout is an effort to orient the view to the scope of the study area. The Dominican Republic shares the island of Hispaniola with Haiti. The island is not far from Columbia, Venezuela, Cuba, Puerto Rico, and the United Stats Fig. 1.2 Context within the Dominican Republic There Dominican Republic consists of 31 provinces, of which Distrito Nacional is one municipality inside a single province. Fig. 1.3 Context within Santo Domingo Distrito Nacional is a municipality within the province of Santo Domingo. There are several other cities within the Santo Domingo Province, including Santo Domingo Oeste, Este and Norte. There are about 100 neighborhoods within the Distrito Nacional. However, because there are different categories of sub neighborhoods, census and spatial data do not show the same number of neighborhoods. The study area is limited to 11 neighborhoods in the eastern part of Distrito Nacional. At the top of the study area, the Isabela and Ozama Rivers converge before they empty to the Caribbean Sea. Fig. 2 Percent of population in poverty in study area Based on a CAD map from the RESURE project, this map shows the concentration of poverty along the rivers. This data was gathered in 1993 and published in Fig. 3 Focus area for assessment: Over 40% poverty Since poverty levels are the highest indicator of vulnerability, the study area will focus only on those neighborhoods with poverty levels above 40%. This limits the study area to neighborhoods along the rivers, which are also the most at risk of environmental hazards, such as floods and solid waste. Fig. 4 Layers created from CAD files Based on CAD maps from the RESURE project, each one of these shapes was extracted from a drawing and imported into layer of its own. The limitation of this map is the features were either polygons or lines, therefore the symbology is difficult to interpret. With more time these features could have been converted into points so the legend can have the same shapes as the features. Fig. 5 Environmental risk: proximity to river After the distance from the river was measured and a raster was created, the raster was reclassified and given a color scale to represent the areas of highest risk. For the purpose of this exercise, and visual consistency, every analysis run with the spatial analyst was at 1000 meters. There is no reason other than visual communication for this number. 30

31 Fig. 6 Environmental risk: proximity to a cañada Cañadas are both a physical and social term in the Dominican Republic. Translated literally to mean canyon, the cañada is also the place where the most informal housing is found. Cañada also means slum. Fig. 7 Environmental risk: proximity to a floodplain Because of the two large rivers in the area, there are parts of the soil that are prone flooding. However, people still build on these areas and are at high risk. Fig. 8 Environmental risk: proximity to an uneven surface This layer, also taken from the RESURE maps, is a rough translation of tierra accidnentado, which roughly translates to uneven land. Not knowing the exact nature of this definition creates a limitation on the importance of this layer. Fig. 9 Environmental risk: proximity to a steep slope The limitation of this map is the exact pitch of the slope is not known. However, the slopes do correspond with the slopes in the digital elevation model (see Figure 17). Fig. 10 Areas most at risk because of environmental conditions When all the distances were calculated and reclassified, they were then combined using the raster calculator to determine the areas that are most at risk because of natural physical conditions. Fig. 11 Social vulnerability: distance from education facilities The distance from the education facility was calculated and reclassified at 1000 meters (each colored ring represents 100 meters). However, the limitation of this layout and the following vulnerability layouts is the exact use is not known. For example, it is not known what type of education facility this features represent. Fig. 12 Social vulnerability: distance from health center There are several health centers, clinics perhaps, in these neighborhoods. But what kind of clinics there are is not known. This layer also does not reflect the fact that there is a very large hospital in the Eschanche Luperon neighborhood, which is in the center of the study area (but no along the river). Fig. 13 Social vulnerability: distance from markets This layer is also limited because the exact nature of the market is not known. Fig. 13 Social vulnerability: distance from police stations For purposes of this exercise, the police station is considered a social benefit. Though I am not sure of the political tension and power struggles in the Distrito Nacional, which could make these a risk to the community if the police are corrupt. Fig. 14 Social vulnerability: distance from public transportation It is not known if these are all the transportation routes, or if they are still in existence. A major change that is not reflected in these maps is the installation of a metro-rail that goes 31

32 along and under Avenida Maximo Gomez all the way to Santo Domingo Norte. Depending on the location of the new stops, this could increase access to many marginalize communities. Fig. 15 Ares most vulnerable because of social conditions When all the distances were calculated and reclassified, they were then combined using the raster calculator to determine the areas that are most vulnerable because of their distance from public facilities. This is a very limited assessment and with the correct data a more accurate vulnerability assessment would include more detailed socio-ecconomic and demographic information. Fig. 16 Both assessments combined: Areas most vulnerable and at risk As demonstrated in Figure 21, this image represents the risk and vulnerability assessments combined. They were given equal weight and combined with the raster calculator. The areas that are at highest risk are the low laying areas in the floodplain and along the steepest slopes. The lease vulnerable areas are long the major transit corridors and higher elevations. Fig. 17 Digital elevation model of study area This shows the general topography of the study area; the darker the cell, the steeper the incline. Fig. 18 Risk and vulnerability in relation to topography Overlaying the risk and vulnerability analysis with the digital elevation model (DEM) shows that the risk and vulnerability assessment corresponds with the physical topography. Fig. 19 Fig. 20 3D image of study area, facing west 3D image of area with highest risk and most vulnerability Fig. 21 Steps of a risk and vulnerability assessment using GIS This figure is a visual to convey, in a very basic way, the process of creating the risk and vulnerability assessment. After the distances were measured with the spatial analyst, and reclassified, the raster calculator tool was used to combine the layers with the following weights. The given weights are arbitrary and the same assessment was run with all at equal weights and there was not much visual difference. Risk assessment: Cañadas 10%; Rio Isabela 10%; Floodplain 25%; Uneven ground 20%; Steep slope 35% Vulnerability assessment: Transporte 15%; Market 10%; Educacion 25%; Policia 20%; Salud 30% Risk and vulnerability assessment combined: Risk 50%; Vulnerability 50% 32

33 CONCLUSION My conclusion is two succinct parts: (1) a conclusion about the process of converting data from computer animated design drawing files to geographic information systems shapefiles, and (2) a conclusion about the results of the risk and vulnerability assessment. Part 1 The strength of GIS is also its greatest weakness: the availability of data. This project was more a lesson in data management and manipulation than actual results. The most valuable part of this project was learning how to use tools and techniques we learned in class, but in different ways. The process of importing, georeferencing and resizing all the layers was laborious, but it emphasized GIS s flexibility, when one understands the possibilities. This also is a cautionary example of how data can be manipulated, whether intentional or unintentional. Since cannot guarantee the accuracy of the original CAD files, I certainly cannot guarantee the accuracy of the newly created shapefiles. If nothing more, the methodology might serve as a how-to when one has unreferenced spatial data, whether raster or vector, and would like to import it into a GIS shapefile. However, the accuracy of the newly created shapefile is only as good as the data it came from. Part 2 By using CAD drawing files I was able to show how a variety of basic social and environmental factors can be analyzed to assess risk and vulnerability in the Distrio Nacional. Governmental institutions can benefit from collecting and creating data using GIS because it is not only easy to create maps, but it is easy to transfer and improve data. Located at the confluence of two large rivers and on the shores of an island nation, and with a population approaching one-million, the Distrito Nacional of the Domincan Republic embodies many unique social and environmental conditions that make understanding, assessing and addressing the needs of the community a challenge. While there are many limitations to this particular assessment, it does serve as a model for how future risk and vulnerability assessments may be approached, particularly with more extensive data. 33

34 REFERENCES Atlas Barrial del Distrito Nacional; Circunscripsción No.3, Ribera Isabela-Ozama, Dirección General de Planeamiento Urbano, Ayuntamiento del Distrito Nacional, 2006 ADN. Ayuntamiento del Distrito Nacional, República Dominicana <http://adn.gob.do/ > Chantada, Amparo "Medio ambiente, crisis y desarrollo: reflexiones en torno a los Ríos Ozama e Isabela." In Antología Urbana de la Ciudad Alternativa. CONAU GEO Santo Domingo: Perspectiva del Medio Ambiente Urbano. Santo Domingo: Consejo Nacional de Asuntos Urbanos. Navarro, Andrés and Ayacx Mercedes "Organizaciones barriales, mejoramiento urbano y desarrollo de la ciudad." In Antología Urbana de la Ciudad Alternativa. Navarro, Andrés " Introducción," in Plan Cigua: Plan de desarrollo para La Ciénaga y Los Guandules. Navarro, Andrés "Desarrollo urbano en República Dominicana: preocupaciones y expectativas." Oficina Metropolitana de Servicios de Autobuses, Santo Domingo, República Dominicana http <://www.omsa.gob.do/ > Pelling, Mark "Assessing urban vulnerability and social adaptation to risk: Evidence from Santo Domingo." International Development Planning Review. Risk and Vulnerability Assessment Tool (RVAT). NOAA Coastal Services Center. RESURE Plan Director, Plan de Accion Cooridnada Interinstitucional para la Reestructuración Social, Urbana, y Ecológica de los Barrios Marginados que Rodean los Rios Ozama e Isabela. Ayuntamiento del Distrito Nacional Sletto, Bjorn Diagnóstico participativo sobre las causas, riesgos y consecuencias de las inundaciones en la cañada Los Platanitos, Municipio Santo Domingo Norte. The University of Texas at Austin, School of Architecture, Center for Sustainable Development. Yunén, Rafael Emilio "La ciudad del presente." In La Ciudad en el Tiempo, pp Santo Domingo: Ciudad Alternativa. 34

35 APPENDIX 35

36 APPENDINDIX This is a valuable lesson in working with data from Latin America because there are many more architects and engineers than there are planners or demographers. If anything, I have learned in detail how attributes and shapes work together in GIS. Otherwise we could just use AutoCAD. Need to convert polygon to points. DATA SOURCES Census 2002 Oficina Nacional de Estadística, Secretariado Técnico de la Presidencia, República Dominicana GIS Shapefiles Ayuntamiento del Distrito Nacional (collected by Dr. Sletto) Caveat: Most the data is defined and projected using "WGS 1984 UTM Zone 19N" as the projected coordinate system and "GCS WGS 1984" as the geographic coordinate system, but reviewing the shapefiles in ArcCatalog, some data will have to be defined and projected. Got world map from ESRI Ensured Map from ESRI is projected Resized and clipped to new layer to Caribbean extent and exported to save memory Projected Coordinate System: WGS_1984_UTM_Zone_19N Geographic Coordinate System: GCS_WGS_1984 Separated Haiti from Hispanola. Muted color (Haiti is for geographical reference only) Created Major Roads file : "NOMBRE" = 'AVENIDA HNAS. MIRABAL' OR "NOMBRE" = 'AVENIDA JHON F. KENNEDY' OR "NOMBRE" = 'AVENIDA MAXIMO GOMEZ' OR "NOMBRE" = 'AVENIDA ABRAHAN LINCOLN' OR "NOMBRE" = 'PADRE CASTELLANOS' OR "NOMBRE" = 'AVENIDA EXPRESO V CENTENARIO' OR "NOMBRE" = '27 DE FEBRERO' OR "NOMBRE" = 'PARIS' OR "NOMBRE" = 'AVENIDA LAS AMERICAS' OR "NOMBRE" = AVENIDA LUPERON OR "NOMBRE" = 'AUTOPISTA DUARTE' OR "NOMBRE" = 'SAN VICENTE DE PAUL' How to convert CAD drawing file to GIS layer (dwg to lyr) Create a new polygon from CAD.dwg (i.e. Importing from CAD) In ArcCatalog Open Toolbox: - Search CAD - Double-click Import from CAD - Input.dwg you want to use (Ex: pobreza suelos ) 36

37 - Output to preferred file - Defined spatial reference to others - OK Suelos > Create layer - In ArcCatalog, click on Suelos.dwg, right click on Suelos.dwg > select create layer > chose location > open in ArcMap (Georeference later, when moving shapes) #Create new layer from CAD.dwg In ArcCatalog - Click on.dwg file in table of contents so all layers display - In preview see which layer is desirable (usually polygon and annotation) - Right click and Export - Export to shapefile (single) - This layer can be georeferenced if you want to create new polygons/lines/points by tracing - This layer is good for viewing and referencing imported shapefiles from.dwg in ArcMap. In ArcMap - Add data > select newly created polygon - View > Toobars > Georeferencing > Choose layer) - Georeferencing: - In table of contents, right click layer that accurate, select zoom to layer o In this view, select Georeferencing > fit to display o Add anchor points. Create features from AutoCAD - with sub-barrios 7 underneath AutoCAD layers, set sub-barrios 7 as the only selectable layer. - Select barrios corresponding to hatching (Pobreza: %, 40-50%, - Export to new layer (save as Pobreza_??-??_porciento.shp ) - In ArcCatalog, copy shape and save as Pobreza_??-??_porciento_modified.shp - Using Editor tool, modify layers (barrios) to match CAD drawings (only a few need some minor changes. Add Pobreza field in attribute table to each barrio of each layer - Open attribute table > Options > Add field > name: Pobreza > type: long integer > 100 precision > ok - Start editor > select target layer > enter data under pobreza field (Ex: 100 in all cells of Pobreza_50-100_porciento_modified, 50 for Pobreza_ ) Merge Layers - Input Datasets (all Pobreza_... layers) - Output layer Pobreza_five_merged 37

38 Display - Select Pobreza_five_merged > properties > symbology > show, quantities > fields: values pobreza, normailization none Terreno: (after all selected: data > export > new layer) Accidentado - Select by attribute "EntID" > AND "EntID" < Add to current selection "EntID" > 9413 AND "EntID" < Inundable - Select by attribute "EntID" > AND "EntID" < Add to current selection "EntID" > AND "EntID" < Add to current selection "EntID" = 1 - Add to current selection "EntID" = Revove from current selection "EntID" = Regular - Select by attribute "EntID" > AND "EntID" < Add new field into each terreno layer called Ter_tipo > add corresponding names into column (i.e. inundable, accidentado, regular (After adding new field, start editor and replace all blank cells using options > find and replace > replace all Merge Accidentado, Inundable, Regular into one shapefile - Toolbox > merge > Display unique values - terreno_merge properties > symbology > categories > unique values > value_field = ter_tipo Create Rio Isabela shape: (done need to describe process somehow got it from pobreza ) - add Rio Isabela Convert Transporte Files (looks like it has ca#adas) (Maybe Vial is better) (Converted Not able to use the above ) Convert Equipamento Comunitario - Import from CAD > input/output/define/ > OK > - Open Geodatabase in ArcMap > add line, add points - Using ArcMap, select a point in the.dwg legend and open attribute table, view only selected note EID - In ArcCatalog open TxProp in Priview mode and find that EID and note properties: (Centro de Acogida, Salud, Iglesia, Puesto Fijo de INSPIRE, Comedor Economico, Deporte/Club, Policia, Educacion, Mercado) - Find shape in.dwg map an select like shapes, export to new layer (repeat for each shape in legend 38

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