Master Thesis submitted within the UNIGIS MSc programme Interfaculty Department of Geoinformatics - Z_GIS University of Salzburg

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Master Thesis

submitted within the UNIGIS MSc programme Interfaculty Department of Geoinformatics - Z_GIS

University of Salzburg

Development of a Fire Mapping and Analysis tool for Windhoek

by

Elago Nantana

STUDENT NUMBER 1050298

A thesis submitted in partial fulfilment of the requirements of the degree of

Master of Science (Geographical Information Science & Systems) – MSc (GISc) Advisor:

Ann Olivier

Windhoek, Namibia, April 2018

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Acknowledgments

At the outset, I would like to thank God, our Father, and Lord Jesus Christ for affording grace and peace upon me. Second, I acknowledge, with particular thanks, the advice, help, and support I have received from Namib Geomatics Technologies team as well as The City of Windhoek Emergency Services division for all their support and for dispensing data for this research along with a full reference made to all published and unpublished sources used.

My absolute and faithful appreciation goes to Ann Oliver for her devoted support and encouraging mindset during my studies.

A big thank you to my family, friends, and colleagues, those who will read and use this thesis in any way. Thank you for your time and for reviewing my work.

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List of Figures

Figure 1.1 Study Area (Windhoek township map) ……….. 10

Figure 2.1 Example of a Fire map (Esri, 2007) ……….……….…….. 16

Figure 2.3 Hot Spot Map (source: Chainey) ………..………. 21 Figure 2.4 The role of GIS and DBMS (Paul A Longley, 2004). ………..……….. 22

Figure 3.1 Fire investigation report with fields (City of Windhoek) ………..…….……….. 26

Figure 3.2 Monthly fire reports ……….……….. 27

Figure 3.3 Yearly fire reports ……….……….……….. 27

Figure 3.4 Database Development Structure ……….. 28

Figure 3.5 Database Structure (screenshot). ………..….………..……….…….. 29

Figure 3.6 curate_street_names.py ……… 36

Figure 3.7 add_township_info.py………..………37

Figure 4.1 Raw Fire incidents reported ………... 45

Figure 4.2 ARCMAP showing geocoded incident points……….….. 46

Figure 4.3 Raw Fire incidents reported………..….... 48

Figure 4.4 Geocoded data with Locations………...……….. 49

Figure 4.5 Fire Incidents per Suburb………..………….……….. 51

Figure 4.6 Fire Incidents per Year………..………….……….…….. 52

Figure 4.7 Total formal structure fire incidences ……….……….. 53

Figure 4.8 Total informal structure fire incidences………...……….. 54

Figure 4.9 Yearly Township statistical analyses………..……….. 55

Figure 4.10 Yearly graphical statistical analyses………..……….………... 56

Figure 4.11 Collective events ……….….….. 58

Figure 4.12 Collect events map……….……….……..……… 59 Figure 4.13 Hot Spots showing inverse distance weighted (IDW) and Getis-Ord Gi* at radius of 100 .………...

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6 Figure 4.14 Hot Spots showing inverse distance weighted (IDW) and Getis-Ord Gi* at radius of 230 M ………..

62

Figure 4.15 Hot Spots showing inverse distance weighted (IDW) radius of 450 M……….………...

63

Figure 4.16 Kernel Density Estimates and Optimized Hot Spot Analysis……….………..….. 65

Figue 5.1 Showing the radius distance changing ……….………. 70

Figure 5.2 Getis – OrdGI* 100 M and IDW 100 M………..….. 72

Figure 5.3 Getis – OrdGI* 230 M and IDW 230 M………..….. 73

Figure 5.4 Getis – OrdGI* 450 M and IDW 450 M………..……….. 75

Figure 5.5 Kernel Density and IDW 450 M………. 75

Figure 5.6 Fire incidences, IDW 230M Optimized Hot Spot Analysis……….………. 76

Figure 5.7 IDW 450 M and collective event……….. 77

Figure 5.8 Getis – OrdGI* 450 M and optimised hot spot analysis……….……….. 78

Figure 5.9 Spatial Autocorrelation by Distance report……….. 79

Figure 5.10 Global morans summary………..……….. 80

Figure 5.11 incremental report ………. 80

Figure 5.12 PROPOSED INCIDENT REPORT……….……….……….. 83

Figure 5.13 How fire incidents could potentially be visualised on a web application……….. 84

Figure 5.14 Simplest architecture of a web GIS (source WebGIS) ………... 85

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List of Tables

TABLE 1THE DATA MODELS FOR FIRE INCIDENT MAPPING……….………31

TABLE 2THE DATA MODELS FOR FIRE EXTRA DATA……….………..………. 33

TABLE 3GIS DATA AND ITS SOURCES……….……….….…….39

TABLE 4CODED DATA……… ………51

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ABSTRACT

It came to my attention that the Division of Emergency Service does not utilise geographical information systems (GIS) or map their fire incidences. The need to map all incidences motivated me to research it, as there are no fire mapping data available currently for analysis; at the same time, the proposed GIS fire mapping tool is a suitable platform to visualise fire data. Not only will fire Hot Spots be easily identified, the fire brigade will also be able to position its data more efficiently. The study will be conducted using different GIS analysis tools, such as Kernel Density Estimation (KDE), Inverse Distance Weighting (IDW), Hot Spot (Getis – OrdGI*). They will be used in order to map all fire incidents in Windhoek. The fire incidences, once mapped, will provide a fire relationship in Windhoek.

Fire mapping is still not practiced in Namibia, but it is hoped that once most fire brigades’ fire incidences are all digitised, fire mapping will grow to become an integral part of all emergency services tasks. A fire mapping tool would enable Fire brigades to provide the exact locations of where fires had occurred. Fire fighters will integrate field collected data with different GIS data in an attempt to understand why certain fires are associated with certain areas. This fire mapping tool would then provide fire fighters with enough information to predict fires up to a certain extent, such as the next veldfires or structure fire.

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CHAPTER 1 INTRODUCTION

1.1 Introduction and Background of the Emergency Management Division

Windhoek is the capital city of Namibia and its fastest growing town; and it will be the research’s study area (see Fig.1). Namibia is located on the south-west coast of Africa, between latitudes 17.5⁰ and 29⁰ south and 11.5⁰ and 25.5⁰ east, occupying a total surface area of 824, 295 km². As reported by the Namibia Statistics Agency, the latest population census figures (2011) estimated the population to stand at 2.2 million.

The Emergency Management Division of the City of Windhoek, as a local authority, is responsible for providing services that include suppression, fire prevention, emergency medical services, heavy rescue, water rescue, hazardous material response, and community awareness safety education in an effort to enhance the safety and quality of life of its citizens (Windhoek, 2014). Its vision is spelled out as follows: "innovative urban management to create quality living environment for all generations"

(City of Windhoek, 2012: 5).

The emergency service constitutes a vital part of the daily life of every citizen, be it in Windhoek, Namibia, or any other part of the world. Somehow, this service has always been taken for granted;

perhaps it is just as well, but as time goes on, it is about these dedicated men who perform an essential role in keeping cities and towns safe for its citizens. The realities of the present are very much a part of our past, and a few glimpses into the past eighty-six years may help identify the point at which it now stands.

The primary objective of the Ministry of Regional and Local Government, Housing and Rural in assisting our local authorities with the fighting equipment are as follows: “Improve capacity to deal with and to prevent fires and other emergencies, prevent loss of lives and reduce damage to properties and to increase investors’ confidence in investing in our local authority areas.” It is also expected that local authorities should render emergency services outside their area of jurisdiction with regard to accidents that usually occur on main roads and veldfires in the close proximity. The communities that are predominantly affected by fire outbreaks are the informal settlements that cannot afford the services rendered (Gurirab, 2011).

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10 Figure 1.1 Study Area (Windhoek township map)

In 1919, Firemaster J.E. Richardson submitted weekly reports to Captain G. Kirby, the Town Clerk, on the inspection of tanks, manholes, mains, and various chemical fire appliances. Men and horses were drilled with steam manual, and checks were conducted on places of amusement. In 1920, it was suggested that a small Merryweather fire engine with a 300-gallon lifting capacity be purchased to obviate the expenses of maintaining the steamer and its temperamental habit of pumping air instead of water and keeping horses in feed manners and harnesses. Instead of obtaining a new fire engine, the Firemaster was reprimanded for using a manhole to test the steamer and cause damage to the street surface. The years rolled by, and in 1958, the Fire Department was housed in a building that had been used during the the first world war as soldier barracks as barracks for soldiers. The Fire Department comprised one “Willy’s Jeep Complete fire engine.

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11 At present, the Windhoek Emergency Services is a combination service staffed by 106 personnel. The emergency operations are supported by an extensive network of support services, which include the police, National Forensic Science Institute, Security, Traffic Services, and so on. The operational staff works in a three-platoon system, where 33% of their time is spent on duty. Senior officers and Fire Prevention and Training staff work day shifts with standby duties after hours on a rotational basis.

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12 1.2 Scope

The majority of all fire data are created and maintained by the Fire Prevention Section, and the data are not spatially enabled. The department compiles and inputs information into the Fire Reporting book and use scanned maps generated by outsiders for their route planning and to attend to all emergency activities.

1.3 Study

It is hypothesised that the proposed analysis tool will greatly improve the capability and efficiency of the Fire Prevention Section. Primarily, no fire mapping data analysis is currently in place in the city of Windhoek or Namibia at large, and the manual recording of fire data does not provide the benefits that can be derived from spatially developed systems. The proposed geographical information system (GIS) fire mapping tool is a suitable platform to visualise fire data. Not only will fire Hot Spots be identified easily, the fire brigade will also be able to position its data more efficiently.

1.4 Aim of Study

While there are documented evidences of increasing fire incidents in Windhoek, there is no geographically mapped fire incidents data. Therefore, there is an immense need to understand how fire incidents are distributed around Windhoek. Geographically mapped fire incidents data will assist the emergency services to predict and prevent risks of fire incidents in the town

1.5 Problem Questions

Will the proposed GIS data and its analysis methods help improve fire incident mapping for the emergency services?

1.6 Objective of the study

The study aims to reach the following objectives:

 Build a geodatabase

 Extract the data from monthly reports

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 Correct, capture, and geocode data

 Create GIS analysis

 Visualize incident patterns

 Create demographic data and Different Map

1.7 Benefit of the Study

All personnel in the Emergency Services are cross-trained for both emergency medical and fire suppression operations. They did not attend mapping or GIS training to geospatially record accidents, and thus, this study will improve incident data capturing, fire incident mapping, and better data administration for the emergency services.

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14 1.8 Limitation of Study

Fire Prevention Sections are still using manual hardcopies for the collection and storage of data, which means the fire brigade is not benefiting from the spatial data advantages such as fire Hot Spot identification or fire mapping. The research will only be conducted for the Fire Prevention Section of the City of Windhoek, the reason being to truncate travelling fees and time. The researcher was employed at the City of Windhoek.

1.9 Conclusion

While this chapter clearly explains the research, it also provides a general indication about the introduction as well as the problem statement and objectives of the study. From the above background, an assumption can be made that having the entire fire incident data arranged geographically will make incident mapping an easier process. The following chapter will look into previous studies that made fire incident mapping an easier mapping process.

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Chapter 2 LITERATURE REVIEW

2.1 Introduction

Literature reviews are regarded as evaluative reports of information found in literature related to the selected area of study. The review will describe, summarise, evaluate, and clarify GIS and fire incident mapping and broadly consider the different Spatial Analysis tools for mapping fire and data storage.

Longley (2005) reported in the second edition of Geographical Information Systems and Science that there is some controversy about the history of GIS, since parallel developments had occurred in North America, Europe, and Australia. Much of the published history focuses on the contributions of the US.

Therefore, we do not yet have a comprehensive history of this subject. What is clear, though, is that the extraction of simple measures largely drove the development of the ﬁrst real GIS, the Canada Geographic Information System (CGIS) in the mid-1960s. Since 1969, ESRI has provided customers around the world with the power to think and plan geographically.

ESRI software, the market leader in GIS solutions, is used in more than 300,000 organizations worldwide, including the 200 largest cities in the United States, most national governments, more than two-thirds of Fortune 500 companies, and more than 5,000 colleges and universities. ESRI applications, which run on more than one million desktops and thousands of web and enterprise servers, provide the backbone for the world’s mapping and spatial analysis (GIS BEST PRACTICES, 2007).

Scurry defined Geographic Information System, or GIS, as a computerized data management system that is used to capture, store, manage, retrieve, analyse, and display spatial information. Data captured and used in a GIS are commonly represented on paper or other hardcopy maps. A GIS differs from other graphics systems in several respects. First, data are georeferenced to the coordinates of a particular projection system, which allows precise placement of features on the earth’s surface and maintains spatial relationships between mapped features. As a result, commonly referenced data can be overlaid to determine relationships between data elements. A map is defined as a representation, usually on a flat surface, of a part or whole of an area. The job of a map is to define spatial relationships of specific features that the map aims to represent. There are many different types of maps that attempt to represent specific things, such as political boundaries, population, physical features, natural resources, roads, climates, elevation (topography), and economic activities (DL+, 2013). (see Figure 2.1)

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16 Figure 2.1 Example of a Fire map (Esri, 2007)

The mission of the fire service is to protect life, property, and natural resources from fire and other emergencies. With increasing demand, the fire service must employ the best tools, techniques, and training methods available to meet public expectations. Risk management, preparedness, and mitigation have assumed new importance with challenges facing fire departments today. One emerging tool that helps the fire service optimize emergency services delivery is GIS technology. A fire mapping tool would enable decision makers to identify fire Hot Spots and consequently implement measures for fire awareness. The fire mapping tool can be used in planning how resources at all fire brigade headquarters can be utilised efficiently in the fight against fire. The design of the fire mapping tool should involve as many fire-fighters divisions as possible for it to be user friendly and for it to be able to add value to all fire fighting operations.

Ahrens (2013) defines structure fire as a fire involving the structural components of any type of residential or commercial buildings, ranging from single-family detached homes and townhouses to apartments and tower blocks or various commercial buildings, such as offices and shopping malls.

Arrive Alive (2014) described Natural fires as fires that occur seasonally, and the primary source of ignition is lightning.

Due to the source of ignition, occurrence is most frequent during the rainy seasons. They also define Unnatural fires as fires caused as a result of intervention of humans and purposeful or accidental

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17 ignitions. Due to the source of ignition, frequency is usually higher than natural fires and mostly occur in the dry season, which is often protracted by late rains (Alive, 2014).

Zlatanova (2008) reported that a GIS user could begin to analyse and display incident trends. A spatial query could request incident locations by cause, time of day, specific geographic locations, and so forth. GIS searches the data tables, collates the data that matches the spatial request, and displays it on the map. Incident trend analysis can be performed quickly, displayed logically, and understood easily. These analyses provide decision support information for issues related to fire prevention, staffing requirements, and apparatus placement/deployment. Fire mapping can therefore be implemented as part of a comprehensive National Disaster Risk Management plan initiative.

GIS enable fire brigades to identify the exact location where urban and forest and veldfires occurred in a specific area. The South African government describes how fire mapping is made simple by developing an Advanced Fire Information System (AFIS) to locate fires near real-time over southern Africa (CSIR, 2007). Active fires are detected using data from Moderate Resolution Imaging Spectroradiometer (MODIS) sensor on NASA's Aqua and Terra satellites received by South African National Space Agency (SANSA, Hartebeesthoek) (CSIR, 2007). AFIS determines the exact location of active fires on a map (each dot on the fire map shows an active fire varying in size from 200 m to 1000 m) and sends a text message , enabling Eskom to respond promptly to fires in the proximity of transmission lines to reduce damage and power supply disruptions. This application of remote sensing, coupled with cell phone technology for alert messaging, or SMSs, is the first of its kind in the world (CSIR, 2007).

The paper GIS for the Fire Service (2012) reports that the primary responsibility of a fire department is the delivery of fire and rescue services. The delivery of these services typically originates from fire stations located throughout the area to be protected. To provide effective service, the crew must respond within a minimum amount of time after the incident is reported and with sufficient resources to initiate fire, rescue, or emergency medical activities. Fire station location planning must take into account a number of variables, including the importance of time in responding to fire and medical emergencies, flashover, fire department total reflex time sequence, emergency medical services, when a specific type of fire occurs in a specific area. The area plays a key role in understanding fire and how fire patterns can be discerned. By examining the geographical components of fire, it can be comprehended efficiently. Some methods that can be used in mapping locators include identifying fire patterns, exploring the relationship between fire and socio-economic issues, as well as the environment. This data includes location or position information that is important for public safety mappers. Data types are typically points or polygons, and data sources can include assessor parcels, fire preplans, Environmental Protection Agency (EPA) inventories, or similar datasets (Price, 2012).

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18 Michael Hoose, a fire captain for the Santa Barbara Fire Department, says that a key part of this strategy includes quickly collecting and sharing field information about dynamic fire situations through sophisticated GIS systems (adapx, 2008) and further explains that information is the critical component of successfully fighting and staying ahead of raging wildfires. Conditions can change quickly in the course of a fire, which require effective ways of gathering, digesting, and sharing information. Data collection is conducted in several ways and is gathered by numerous personnel.

Accumulation and dissemination of information is a top priority for incident commanders, who coordinate the firefighting efforts and resources, which can include hundreds of firefighters and a range of resources aiding them. Data about conditions needs to flow easily from the field to the Incident Command Post (ICP), where central resources can be managed and accessed, decisions can be made, and updated plans and conditions can be shared back with field crews. The data needs to be timely, easy to understand, and accessible to all the stakeholders (adapx, 2008).

Johnson (2000) in a White Paper said that GIS allows fire officers to access all necessary deployment data in place. Data can be added, subtracted, or modified with computer mouse operations, and alternative plans can be initiated, analysed, and modelled by a group or staff of fire officers using GIS.

Once a GIS database has been created, deployment analysis can be reviewed and updated at any time with minimum effort. White Paper on GIS best practice says analysing data requires the right tools, and these tools require collected data. The preeminent importance of all is that the data has to be efficiently accessed and secured; this is all achieved through either one of the various available tools on the market or additional GIS functionalities. A good GIS program can process geographic data from a variety of sources and integrate it into a map project.

Different analyses can be used to process events. Fire departments have the responsibility to protect lives and property, but they also have a limited amount of resources. It is crucial that the deployment of resources is effective, efficient, and based on the best information available. Effective deployment is based on numerous complex issues: fire demand, effective firefighting force, occupancy, historical occurrence, response time, and so on (Johnson, 2000). GIS can perform complex incident analysis to indicate trends, illustrate patterns, and identify areas of high call volume. A GIS display of historical incidents (represented by points or icons on the map where they had occurred) includes attribute information for each incident. Attribute information (descriptive data about a map feature) contained in the underlying database can include incident type, cause, date of incident, time, units that responded, and unit arrival times (Esri, 2012).

An incident is an unplanned event or chain of events that results in losses, such as fatalities or injuries, damage to assets, equipment, the environment, business performance, or company goodwill. A near

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19 miss is an event that could have potentially resulted in the aforementioned losses, but the chain of events stopped in time to prevent this. These incidents can be classified in all kinds of severities and types and thus, into categories. Investigation and cause analysis should take these different categories into consideration (cgerisk, 2014).

Incident trend analysis is a common practice by fire departments. With GIS, it can be performed quickly with all the relevant information. GIS can access and geocode (place a point on the map) historical incidents. This capability can be refined by conducting a spatial query of the records management database that specifies the type of incident, time range, or specific geographic area. For example, a GIS user can request to see incidents of arson that occur between the hours 1:00 a.m. and 5:00 a.m. on Saturdays in fire districts 1 and 2 (Esri, 2006). GIS interrogate the records database and place points on the map that meet this request. The GIS user can then access all the information concerning each incident by simply clicking on the incident point. GIS can add additional information by displaying the demographics for each of the two fire districts identified in the spatial request (Esri, 2006).

One fire incident analysis tool is the Fire Incident Mapping Tools (FIMT, version 1.83 Build 3) that was recently released by the United States Forest Service (USFS) as a prototype application distributed for testing during the 2004 fire season. FIMT consists of an ArcMap toolbar that furnishes fire GIS technical specialists (GISTs) with all ArcMap tools they require for managing the GIS data required to produce maps that support a fire incident. As part of FIMT, a standardized geodatabase model was developed for storing fire incident features, and this standardized model provides a consistent framework for all fire GIS personnel using FIMT tools. The geodatabase is created by FIMT from a stored database template installed with the application (Tim Clark, 2005). Overall, the prototype testing that took place during the 2004 fire season has shown FIMT in combination with ArcGIS is a powerful tool for managing fire incident data. The improved cartographic output of ArcGIS and the benefits of a standardized database provide GISTs the kind of advanced GIS tools necessary to support the growing demands placed on geospatial technologies during a high-pressure incident response (T.

Clark, 2005).

A fire mapping tool would enable decisionmakers to identify fire Hot Spots and later implement measures for fire awareness. The fire mapping tool can be employed in planning efficient utilisation of resources at all fire brigade headquarters in the fight against fire.

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20 Density analysis retrieves known quantities of one phenomenon and spreads them across the landscape based on the quantity that is measured at each location and the spatial relationship of the locations of the measured quantities. Density surfaces show where point or line features are concentrated (see figure 2.6). For example, a point value for each town representing the total number of people in the town will assist learning more about the spread of population over the region (1995–

2012). Demographic analysis is a group of techniques used to develop an understanding of the age, sex, and racial composition of a population and how it has changed over time through the basic demographic processes of birth, death, and migration. In the context of human biological populations, demographic analysis uses administrative records to develop an independent estimate of the population (Robinson, 2010).

A Hot Spot analysis tutorial of ArcGIS10.1 reports that most local, regional, and national governments, airports, train stations, and other public places include some form of emergency management and preparedness program. It is vital for these governments and organizations to be ready for emergency situations and be in the best position to safeguard lives and property when emergencies or disasters do occur. The tutorial continues to express that among the analytical methods used for emergency management and preparedness are spatial statistics tools. These tools are especially powerful for emergency applications because of the importance of location in emergency and disaster reporting.

The Spatial Statistics toolbox contains statistical tools for analyzing spatial distributions, patterns, processes, and relationships. While there may be similarities between spatial and non-spatial (traditional) statistics in terms of concepts and objectives, spatial statistics are unique in that they were developed specifically for use with geographic data. Unlike traditional non-spatial statistical methods, they incorporate space (proximity, area, connectivity, and/or other spatial relationships) directly into their mathematics (lrosenshein, 2010).

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21 Figure 2.3 Hot Spot Map (source: Chainey)

Different geodatabases are offered for storing different magnitudes of data (Sterling Quinn, 2014).

Today, virtually all large GIS implementations store data in a database management system (DBMS) – a specialist software designed to handle multi-user access to an integrated set of data. Extending standard DBMS to store geographic data raises several interesting challenges. Databases need to be designed with great care and are to be structured and indexed to provide efficient query and transaction performance. A comprehensive security and transactional access model is necessary to ensure that multiple users can access the database at the same time. Ongoing maintenance is also an essential but very resource-Intensive activity (Paul A Longley, 2004).

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22 Personal geodatabases are a small, nearly deprecated form of geodatabase that stores data on the local file system. The data is stored in a Microsoft Access database, which limits the data stored in the geodatabase (Sterling Quinn, 2014). File geodatabases are a recent way of storing data on the local file system. The data is stored in a proprietary format developed by Esri. A file geodatabase can hold more data than a personal geodatabase – up to terabytes (Sterling Quinn, 2014).

Figure 2.4 the role of GIS and DBMS (Paul A Longley, 2004).

2.2 Fire Mapping in Namibia

Fire mapping is still not practiced in Namibia, but it is hoped that once most fire brigade operations are digitalized, fire mapping will then grow to become an integral part of all emergency services tasks.

A fire mapping tool would enable fire brigades to locate exact locations of fire incidents. Firefighters will integrate field collected data with different GIS data in an attempt to understand why certain fires are associated with certain areas. This fire mapping tool would assist firefighters with enough information to predict fires to a certain extent. Fire incidences on a map will create a fire relationship in Windhoek.

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23 Trigg (2000), in his research, said that formulating national fire policy and planning, monitoring, and evaluating fire management programs requires a wide range of information, including the timing, the extent, and frequency of fires. With such large areas involved, the only way to provide these parameters at a national level is to apply satellite data. He further reports that the main fire information required is to delimit the areas burned, which are detected after the fire rather than on the occurrence and distribution of active fires, which, while interesting, reveal little about the true extent of fire. Burnt areas are detected using a simple change detection technique applied to AVHRR thermal imagery. This identifies burns as pixels that increase significantly in brightness temperature between consecutive image dates as compared to background variation.

MET (2016) reports that burnt area product data are updated every month, mapping and documenting the extent of fires throughout the year. Furthermore, to implement a controlled fire management programme strategically and efficiently, it is important to have updates on controlled burning progress. To overcome the limitations of scale of the Burned Area Product in the assessment of fires, there are two recommended manual mapping options: The first is to process 250 m resolution satellite imagery provided twice daily, and the second is to process of Landsat 5 30 m resolution satellite imagery. Both options will require training of an operator to achieve a high level of GIS and remote sensing skills and knowledge.

2.3 Conclusion

Through the literature review, it was determined that GIS has the capability to obtain the goals set for this study. While several methodologies and variables have been used to map fire incidents, all research studies have developed their own mapping methods. The literature has assisted in understanding data capturing, analysing, and storing fire data. In conclusion, fire mapping did not change over the years in Namibia, and The methods of recording and mapping fire incidents is lacking and not recognized as a necessity. Currently, GIS in the world is more environmental friendly, and geographers, GIS analysts, and all map creators have expanded and improved the quality of the final product. Till the present day, we have witnessed dramatic changes. Many of these can be credited to advances in technology and a shift in focus to more sustainable forms of mapping. In chapter 3, we will take a look at all the processes involved in in the analysis tools for Windhoek fire incident mapping.

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CHAPTER 3 REASERCH METHODOLOGIES

The methodology chapter examines the approaches and tools used in collecting data and sources of data for this research and presenting the rationale for selecting these methods. The chapter begins by introducing data collection techniques, details of the interview techniques, and lastly, the development of tools for mapping fire and data storage.

The research will be conducted using a quantitative data analysis approach due to the powerful abilities of collecting and analysing numerical data. It will concentrates on measuring the scale, range, frequency of fires in the city of Windhoek. The research intends to find a completed solution that covers all aspects in storing, managing, and analysing fire data by building a GIS system based on field collected information. The Emergency Management Division, Fire Prevention Section, Fire brigade, academics, and students will benefit from the results of this thesis, as this will guide the divisions in mapping fire incidents in Windhoek.

3.2 Data Acquisition Techniques

3.2.1 Introduction

This section deals with the data acquisition techniques used for gathering data for the study area and creation of all data sets. The date collection, therefore, starts by introducing the study area identification technique and field visit, followed by the research data set requirements and finally, by the digital layout map acquisition techniques used.

3.2.2 Identification of Study Area

The research is conducted for the Fire Prevention Section of the City of Windhoek, the municipality for the capital city of Namibia. The first step was to define the exact boundaries for the City of Windhoek.

See Figure 1.1 Study Area (Windhoek township map).

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25 3.3 Research Material and Methods

All fire incidences were obtained from the fire department, municipality office, as data is recorded manually in the fire incident field book, and the recorded fire data of the city between the years 2008 and 2015 will be examined and analysed. This data has some location features, such as township or erf Number, Street address or position information, which is important for location. Some data such as fire stations, hospitals, police stations, roads, township, and erven for the study area were available from the geomatics division. The other data, such as social services and farms, were taken from the S- G office. The collection of the secondary data for all the fire data in Windhoek after site visit was carried out by obtaining fire station area of operation hard copy format maps.

At present, when a fire incident is reported to a fire station, it is first recorded in the Emergency Services System software, which contains the software solution that enables multiple first response entities to share critical information when collaborating in the preparation, response, resolution, and review processes associated with daily activities, events, and incidents. It is highly customisable and can be easily expanded to fulfil specific event/incident management needs that may exceed the capacity of the organisation's daily tools. Next, a fire fighter is deployed out to field and a report is recorded in an occurrence book, which is a physical book that is manually written by the officer. A monthly report that has neither the exact location nor a map to indicate the area where the incidences took place is created, which is just a simple note that explains how many incidents took place in a month (See Fig 3.2 and 3.3).

It is clear that there has been a slow uptake to the use of computers in the fire brigade department, as evidenced by the current reliance on paper forms for data collection and analogue storage systems.

This is more apparent in record-keeping, where information pertaining to a particular fire is still recorded on paper as shown in figure 3.1. With a growing population and increasing sophistication in matters related to fire, the investigation report does not support location capturing. The data is then populated on a spreadsheet for statistical data generation and misses out crucial mapping information.

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26 Figure 3.1 Fire investigation report with fields (City of Windhoek)

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27 Figure 3.2 Monthly fire reports

2008/9 Jul Aug Sept Oct Nov Dec Jan Feb Mar Apr May June Total

Vehicle Fires 3 4 3 2 2 5 5 5 2 2 2 5 40

Grass Fires 75 76 62 46 52 31 12 8 0 9 28 46 445

Building Fires 12 14 12 17 5 2 12 3 9 7 8 1 102

Informal Dwelling Fire 10 14 24 10 7 11 7 4 3 8 6 5 109

Total 100 108 101 75 66 49 36 20 14 26 44 57 696

FIGURE 3.3YEARLY FIRE REPORTS

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28 3.4 Database Design and Creation

Data is a critical component of successfully generating fire fighting mapping tool, as this will be the basis of setting up the database containing all the attributes of incident analysis. All large operational GIS are built on the foundation of a geographic database. A simple database might be a single file with numerous records, each of which references the same set of fields. A GIS database includes data regarding fire locations, and shapes of geographic features are recorded as points, lines, areas, pixels, grid cells, or TINs, as well as their attributes. After people, the database is arguably the most important part of a GIS, owing to the costs of collection and maintenance and since the database forms the basis of all queries, analysis, and decision making (Paul A Longley, 2004).

Figure 3.4 Database Development Structure

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29 Figure 3.5 Database Structure (screenshot).

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30

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31 TABLE 1THE DATA MODELS FOR FIRE INCIDENT MAPPING

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32

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33 TABLE 2THE DATA MODELS FOR FIRE EXTRA DATA

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34 3.5 Data Capturing to database

Extracting the data from the monthly reports was performed manually. It was identified that the data could be categorized into the following fields listed below:

• Street_Name – Name of the street where the fire incident had occurred

• Type of Incident – Type of incident that occurred (e.g. Informal Dwelling Fire, Vehicle Fire, etc.)

• Formal/Informal – Whether the fire was formal structure, such as a non-moving asset or informal, which is classified as a shack or movable house

• Suburb – The suburb/township where the fire had occurred

• Date – Date of the fire incident

• Erf Number/Place – Erf Number of the incident or sometimes, a name of a place (e.g. Okalindi Restaurant)

• Cause of Incident – The cause of the fire incident

These attributes were recorded in an Excel spreadsheet; however, most of them had spelling mistakes in the Street Name field. Many records also had no data in the Township/Suburb field.

To address spelling mistakes, a program/script was developed to find the street name from the Windhoek streets shapefile, which closely matched the street included in the fire incident spreadsheet. If a matching street name is found, the values are then stored in a separate column (Curated Street Name).

Upon fixing the street names, another program/script was developed to populate the Suburb field, where there were no values. This program iterated through every row of the fire incidents spreadsheet, and where it found a street name, it would do as follows:

1. Select the respective street in the streets shapefile 2. Select the township that contained the selected street

3. Enter the name of the selected township into the Suburb field of the fire incident spreadsheet

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35 Upon creating these scripts and successfully fixing most records, the fire incidents spreadsheet was then used to geocode on a point and suburb/township level, where a point feature class was produced, which contained points on streets in which fire incidents had occurred.

3.6 Geocoding

Geocoding is the process of transforming a description of a location, such as a pair of coordinates, an address, or the name of a place at a specific location on the earth's surface. You can geocode by entering one location description at a time or by providing a number of them at once in a table (See figure 3.6 -3.7).

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36 Figure 3.6 curate_street_names.py

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37 Figure 3.7 add_township_info.py

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38 Fire incidents data are all available in hard copy, as shown in Table 1.1; it was captured in the geodatabase using street names and erf numbers as point data for each year, and other new data entities, such as fire stations and so on, were created as new features. Different data layers were digitized and prepared in GIS environment, and entities such as fire stations, hospitals, and police stations were acquired from the Geomatics division.

All spatial data used (see table 2) were projected on the spatial reference of SG Data, and the coordinate systems and and map compilation was required before the location data could be digitized.

New_Namibian

Projection: Transverse_Mercator False_Easting: 600000.000000 False_Northing: 10000000.000000 Central_Meridian: 17.000000 Scale_Factor: 1.000000 Latitude_Of_Origin: 0.000000 Linear Unit: Meter

Data Layer/s Source

Street base map Esri / Geomatics

Fire Incidents Fire section

Fire Stations, Digitized by the authors

Hospitals, Police Stations Surveyor General

Road Digitized by the authors

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39

Ervens, Households Geomatics

TABLE 3GIS DATA AND ITS SOURCES

3.7 Hot Spot Analysis

There are various different statistical techniques designed to identify ‘Hot Spots’ (Everitt, 6.2 1974).

Hot Spot Analysis employs vectors to identify locations of statistically significant hot spots and cold spots in the data by aggregating points of occurrence into polygons or converging points that are in proximity based on a calculated distance. The analysis group’s features, when similar high (hot) or low (cold) values, are found in a cluster. The polygons usually represent administrative boundaries or a custom grid structure.

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40 3.7.1 Urban Fire Statistics

This statistical technique is aimed at grouping cases together into relatively coherent clusters. All the techniques depend on optimizing various statistical criteria.

3.7.2 Urban Fire Hot Spots

The Hot Spot Analysis tool calculates the Getis-Ord Gi* statistic for each feature in a dataset.

3.7.3 Fire hot spots Area Coverage

Optimized Hot Spot Analysis executes the Hot Spot Analysis (Getis-Ord Gi*) tool through parameters derived from characteristics of the input data.

3.8 Interview Questionnaire

According to Analytic Technologies (1997), “a questionnaire is a formal written set of closed- and open-ended questions that are asked of every respondent in the study. The questions may be self- administered or interviewer-administered.” In summary of the above, a questionnaire is among the instruments used for data collection.

The questionnaire technique method was chosen to gain an understanding of fire incident mapping from the reporting officers in the department, and the questionnaire contained both open and closed questions to collect data. Open questions allowed for subjective views of the interviewee. The questionnaire was aimed at putting together information on the level of GIS software, fire mapping, fire data availability, fire data management, and accessibility. The questionnaire was much more on the level of understanding fire incident mapping in the fire reporting section. While drafting new fire mapping strategies, understanding fire mapping will be a prerequisite for different firefighters. Fire Prevention Sections are still using manual hardcopies for the collection and storage of data, which means the fire brigade is not benefiting from spatial data advantages such as fire Hot Spot identification or fire mapping. Thus, the questionnaire will advance all parties involved.

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41 3.9 Conclusion

This chapter considered the methods, tools, and resources used to complete this research. The reasons and the importance of using the methods and materials applied were explained through the methodologies. The chapter expanded by looking at the definition of a questionnaire.

By looking at the way in which fire incident reports were reported between 2008 and 2015, it is evident that the process of deriving these reports is tedious, manual, and not uniform. Also, the underlying data capturing techniques limit the overall quality of the data, as there is very little emphasis on the location of fire incidents. This is because, usually, only a street name is provided along with, sometimes, the name of a place. In some cases, an erf number is given, and in most cases, nothing more other than the street name is provided. This makes it impossible to perform detailed analysis, since a street is a linear object and ranges from a few meters in length to a few kilometres, making it impossible to know the exact position of the fire incident. As mentioned in the limitations section, another problem with using street names is that some streets cross multiple suburbs.

A questionnaire was created aiming at the level of understanding of fire incident mapping in the emergency department. The next chapter will provide the results of the set objectives.

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42

CHAPTER 4 RESULT OF THE STUDY

The previous chapters disclosed that the Emergency Service forms a vital part of the daily life of every citizen and that somehow, this service has always been taken for granted, as there are no existing GIS tools for handling firefighting spatial data and no GIS capturing system implemented.

Hence, in today’s age, computers, mapping tools, and applications have become a way of life; it is clear that there has been no uptake to the use of computers in the fire brigade department, as evidenced by the current reliance on paper forms for data collection and storage. This is more apparent in record-keeping, where information pertaining to a particular fire is still recorded on paper, as shown in figure 3.1.

With a growing population and increasing sophistication in matters related to fire, it is immediate and imperative to computerise the storage of fire information. Currently, when a fire incident is reported to a fire station, it is first recorded in the Emergency Services System software, which contains the software solution that enables multiple first response entities to share critical information when collaborating in the preparation, response, resolution, and review processes associated with daily activities, events, and incidents.

In this chapter, the outcome reporting on fire incident data will be considered, which will help with managing data and information dissemination to the public and other relevant stakeholders. A further discussion of the results is included in this chapter and how this data will be incorporated into ArcGIS Desktop 10.5 software as per Data Flow Structure in shown in figure 3.4. Several methods will be explored to analyse fire incidences using different spatial analysis tools to interpret fire data.

4.2 ORGANIZATION INFORMATION

The City of Windhoek has experienced significant population growth over the last 15 years; from a population of approximately 180,000 in 1995 it has grown to over 350,000 in 2010. The City’s emergency management facilities, such as the fire brigade, and resources have not kept pace with this growth and maintained the same staff complement of firefighters and ancient facilities for addressing the ever-increasing and challenging responsibilities of firefighting (Windhoek, 2010).

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43 The dispatching centre, located at the Headquarters Fire Station, is staffed round the clock by emergency communication technicians. All emergency apparatuses are equipped with mobile radios, allowing voice and data communications while en route to a scene of emergency. In addition, crews are equipped with portable radios to aid personnel safety and emergency scene communications. A repeater, positioned strategically, ensures complete radio coverage in Windhoek. Emergency operations in the City of Windhoek’s Emergency Services are supported by an extensive network of support services, which include the police, National Forensic Science Institute, security, traffic services, and so forth.

The present strength of the Brigade is 106 full-time personnel. The operational staff works in a three- platoon system, which means that 33% of their time is spent on duty. Senior officers and Fire Prevention and Training staff work day shifts with standby duties after hours on a rotational basis.

There are four Emergency Management stations in Windhoek (see figure 1.1) with an emergency response time of 5–15 minutes to the affected area.

The Windhoek Headquarters fire station covers Windhoek North, West, East, Eros, Klein Windhoek, part of Katutura, and farms along the B6 truck road to the airport and B1 to Okahandja.

Diaz fire station’s area of operation covers Ausblick, Olympia, Kleinne Kuppe, Prosperita, Cimbebacia, Academia, Pioneerspark, Hochland Park, Dorado, Windhoek south, and the farms along B1 road to Rehoboth.

Maxuilili fire station’s area of operation is okuriangava, Hakahana, Havana, Katutura, and lafrenz.

Finally, Otjomuise fire station covers Otjomuise and the surrounding informal settlements, Khomasdal, Goreangab, Rocky-Crest, farms on C28 Main Road to Dan Viljoen.

All the data used for this research was provided by the Windhoek Headquarters. Primarily, no fire mapping data analysis is currently occurring in the Windhoek or in Namibia at large, and the manual recording of fire data does not provide the benefits of spatially developed systems. The proposed GIS fire mapping tool is a suitable platform to visualise fire data. Not only willfire Hot Spots be easily identified, the fire brigade will aslo be able to position its data more efficiently. While the City of Windhoek is actively involved in mapping the land within the city, this research, focusing only on fire incidents, will provide utility for the Emergency Services.

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44 Fire incident data of the emergency department are diverse and complex; therefore, the City of Windhoek requires a good analysing tool for its geographic data. This research attempts to confirm these statements.

The fire incidences on the new maps will create a fire relationship in Windhoek. Fire mapping is still not practiced in Namibia, but it can be anticipated that once most fire brigade operations are digitised, fire mapping will grow to become an integral part of all emergency services tasks. A fire mapping tool would enable Fire brigades to indicate the exact locations of fire incidents. Firefighters will then integrate fire field collected data with different GIS data to understand why certain fires are associated with certain areas. This fire mapping tool would provide firefighters with enough information to predict, up to a certain extent, the next veld or structure fire.

4.3 MAPPING METHODS

4.3.1 PERFORMING URBAN FIRE STATISTICS

A well-established GIS analysing tool is required to help the researcher create a set of solutions to meet his goal, as this will measure against the extent to which the developed system satisfies its objectives. The literature review broadly explored different spatial analysis tools for fire mapping and data storage, and it was clearly stated that a fire mapping tool should be able to capture, store, manage, retrieve, analyse, and display spatial information in several layers at a time by integrating other GIS data and fire date in the maps.

ArcGIS enables users to visualize, manipulate, analyse, and display spatial data (see figure 2) that is extracted from the monthly fire incident Word document reports (see figure 4.1). For these data to be analysed, the researcher had to create shapefiles by converting populated Excel documents in ArcGIS.

However, most of the data contained spelling mistakes in the Street Name field, and many records also provided no data in the Township/Suburb field; hence, it was left with null values (see figure 4.3).

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45 Figure 4.1 Raw Fire incidents reported

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46 Figure 4.2 ARCMAP showing geocoded incident points

There are various software and GIS applications that can be utilised, and for this research, Word was used, as all fire incidences are all recorded in this format, and extracting the data from the monthly reports was performed manually. Excel was used to create fields and attributes to identify the data to be categorized, and Quantum GIS was used to write the coding program/script, which was developed using and ArcGIS for analysis and map generation. All this software performed a key role in point

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47 analysis that was used to obtain results from analogue reports, as this process has to first refine all the unedited data followed by an interrogation of all the records in the new database before placing them on the map, as explained in the research methodologies by extracting the type of incident occurred (e.g. Informal Dwelling Fire, Vehicle Fire, etc.). Formal/Informal fires are classified by determining whether the fire was formal structure, a non-moving asset, or informal structures, a shack or a movable house (see statistics on Figure 4.7).

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48 Figure 4.3 Not coded Fire incidents reported

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49 Figure 4.4 Geocoded data with Locations

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50 For this research, all fire incidences were manually counted in each township per year (see table 4.1).

Figures 4.5 to 4.9 below exhibits the best statistical summary and maps of the reported fire incidents between 2008 and 2015 per suburb. Based on the summary, most fire incidents were reported in 2011, 2012, and 2013. Generally, the highest fire incidents were recorded in Okuryangava, Windhoek, Katutura, and Khomasdal townships. 85 out of 527 fire incidents, however, could not be allocated to any suburb/township and are, therefore, categorised as Unspecified.

2008 2009 2010 2011 2012 2013 2014 2015 Total

Academia 1 0 0 0 3 2 0 0 6

Auasblick 0 0 0 1 0 0 0 0 1

Cimbebasia 0 0 2 1 1 0 2 0 6

Dorado Park 0 0 1 7 0 0 0 0 8

Eros Park 0 0 1 0 0 0 0 0 1

Goreangab 0 1 2 3 14 12 1 7 40

Hakahana 0 0 1 4 7 9 0 4 25

Hochland Park

1 0 1 2 3 4 1 2 14

Katutura 0 4 4 13 13 12 5 7 58

Khomasdal 0 2 5 6 16 10 0 3 42

Klein Windhoek

1 3 1 1 1 3 3 2 15

Kleine Kuppe 0 0 0 4 0 0 2 0 6

Lafrenz 0 0 0 0 0 1 0 0 1

Okuryangava 0 5 2 13 28 17 7 13 85

Olympia 0 0 0 2 3 0 0 0 5

Otjomuise 0 3 2 7 7 1 1 1 22

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51

Pionierspark 0 2 1 0 4 3 1 2 13

Rocky Crest 2 0 0 0 1 0 0 0 3

Wanaheda 1 1 3 4 2 0 3 4 18

Windhoek 2 6 6 6 24 14 2 13 73

Unspecified 11 9 6 18 18 11 0 12 85

Total 19 36 38 92 145 99 28 70 527

TABLE 4CODED DATA

Figure 4.5: Fire Incidents per Suburb

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52 Figure 4.6 Fire Incidents per Year

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53 Figure 4.7 Total formal structure fire incidences

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54 Figure 4.8 Total informal structure fire incidences

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55 Figure 4.9 Yearly Township statistical analyses

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56 Figure 4.10 Yearly graphical statistical analyses