Organised Editing/Activities/LocalImpactGovernanceActivity Zambia/DAI Zambia local Impact Governance Project

Summary

Implementation of the “Service Delivery for Central and Muchinga Province  Mapping” project has been an immense learning opportunity for the entire project team. From the inception work,  creating a workflow, documenting the methodology and OSM data to designing and stakeholder meetings with a number of local communities, provincial planners, district planners, and ward development committees, HOT and Local Impact have developed new approaches to better identify optimal data collection methods and tooling by making sure that stakeholders have a more holistic understanding of the project. Through the support of Local Impact different ministries and government agencies were brought on board as partners in the implementation of the project. Generally, community stakeholder engagement and field mapping activities are important tools for building strong, resilient, and sustainable communities. For future consideration, notable  recommendations are increased training in QGIS  with planners to ensure that there is suitability for the use of the data that was provided to them; there is a need to have a well formulated plan for data sharing from the national level to the districts to ensure data is used as widely as possible; consideration of increased training for data use at various points in the project lifecycle and finally improved planning and resources dedicated to both the field mapping phase and quality assurance activities for the project will overall improve the quality of outputs for the project.

Introduction

The Local Impact Governance Activity (Local Impact) is a five-year (2020-2025) USAID project that aims at supporting the Government of the Republic of Zambia’s (GRZ) decentralization agenda by strengthening local governance and devolved service delivery, and ensuring they are transparent and responsive to citizen needs. The objectives of the project are to improve citizen's collective skills to effectively assess service-delivery needs and performance and constructively engage with government; to ensure that the sub-national governance system becomes more responsive to citizen needs, generates, and accountability expends revenue and improves service-delivery outcomes, and the program generates learning and facilitates problem-solving that supports the development of an effective, financially viable sub-national governance system for devolved services, capable of being scaled up throughout Zambia. HOT has been contracted to do the service-delivery mapping component that is helping the local authorities in both Muchinga and Central province to complete their Integrated Developmental Plans (IDPs), in order to understand what is available in the two provinces a desk review focused on identifying existing geospatial datasets and other existing data both at national, provincial and district levels. The process involved reviewing documents and accessing spatial datasets to understand existing data in relation to service delivery. The desk review further included identifying existing data portals in Zambia that are used for sharing data nationally. Stakeholder mapping and identification were also carried out during the desk review phase. This stakeholder mapping included identifying data custodians who would need to be involved in data collection activities at various levels. During the stakeholders meeting it was discovered that most of the data that was proposed on the data model was available in different government ministries such as the ministry of lands, electoral commission of Zambia (ECZ), and Zambia Statistics Agency (Zemsta). With the help of the stakeholders, most of the data sets were shared such as school, health, vet comp, boreholes, business data set, agriculture camp, ward boundaries, and district boundaries. Using high-resolution imagery collected and additional datasets, HOT remotely mapped over 1,000,000 buildings, and 12000 km of roads, and uploaded 3000 place names into OpenStreetMap for Muchinga and Central provinces. Using datasets that were collected and available data in OpenStreetMap, HOT developed a methodology and workflow to compare, integrate and upload this rich information into OSM. Following the data model for Integrated Development Plan (IDP) it was concluded that HOT is going to collect information on water and waste management because other data sets were available there was no need to start collecting the same information. The following section provides more detail into the contents of the respective data collection forms used by data collectors during field mapping:

• Water collection form - used to collect water information for the community, the communal water points area, the type of water source, contact number and the estimated population accessing the water point. The collected information was then used to identify and highlight the community that has access to water, and the kilometers that people move just to reach the water source.
• Waste collection form - used to collect information on the dumping/waste disposal sites. This collected information was then used to show the location of the waste disposals and dump sites in the district, which one is illegal and legal, and how the collection of the waste is done.

During the one-month and two-week field mapping exercise, HOT engaged over 200 local volunteers, community members, and leaders in the mapping of over 1,000,000 buildings, 3000 Place names, and 12000 km of roads across Muchinga and Central Province.  Extensive stockholders meetings were carried out with each of the provinces participating to ensure there was local buy-in and understanding and acceptance of the project objectives, field activities, and intended outcomes.

This report outlines the processes undertaken to ensure quality control and assurance of data cleaning and validation approaches were central to the implementation of this project and its life cycle. It also outlines the lessons learned through the implementation of the project and what we believe are important considerations to make when designing future projects that are similar in scope.

Data Model

The data model for this project was developed with consideration of the Ministry of Local Governance Integrated Development Planning Guidelines in relation to the project’s survey and spatial data collection activities. As part of the activity's inception, consultations with key stakeholders and a desk review of empirical and spatial data sets were conducted. Stakeholders that were involved at different levels of the consultations ranged from Local Governance, Government spatial agencies, local authorities, and civil society groups. Contributions and feedback from these meetings and workshops were included in the inception report. Based on inputs and existing data sets, a data model to guide the type of data collected was developed. The table below shows how the project data model was developed based on the Integrated Development Plan guidelines.

Key IDP Thematic Area	Included in Local Impact Data Model (Y/N)	Area Of Focus/Attributes in data model	Comment
Education	Yes	Pre-school Primary Secondary Tertiary College University
Health	Yes	First level hospital Second level hospital Third level hospital Rural health center Urban health centre Rural Health post Urban Health Post Clinic Health Post Pharmacy
Water & Sanitation	Yes	River Lake Public borehole Protected well Unprotected well Water kiosk Communal tap	The Local Impact Data Model combined Water and Sanitation thematic areas into a single form.
Solid waste disposal	Yes	Liquid waste (wash water, waste detergent, etc) Solid rubbish (plastic, paper, tins, glass, ceramics) Organic waste (food, garden, rotten meat, manure) Recyclable rubbish (can be converted and use again) Hazardous waste (flammable, toxic, reactive, etc)
Economic	Yes	Business: Bank Mobile Service Microfinance Market Mall Commercial Entertainment Fuel Filling Station Financial Services Factory Warehouse Recreational Facility Agriculture: Agriculture Market Agriculture Camp Solar hammer mills FRA depot Vet camp Agriculture structures (eg. SIlo, poultry, piggery, etc) Fish camps Other Features: Historical Sights Monuments Hotels Restaurants Tourism Service Telecom Towers Community Radio Stations Airports Aerodromes Abattoirs Religious Facilities Cemetery Administrative Office (Council/Traditional)	The Local Impact project Data Model contains three separate forms for this thematic area: Business Agriculture Other Features
Roads and transportation	Yes	Primary (linking provinces) Secondary (linking districts) Tertiary (linking towns) Residential (linking communities)
Community Facilities	Yes	N/A	This thematic area doesn’t have a specific form. But some of its attributes are covered across several other forms.
Communication	No	N/A	The Local Impact project Data Model is not collecting data specifically on Communication. This is because data on communication towers/masks is available with ZICTA and all mobile service providers (Zamtel, MTN, Airtel). The IDP report also recommends that this data is collected from these institutions.
Energy (Electricity/ Hydro/ Solar)	No	Access to electricity Type of Power Electricity Hydro Solar Reliability of service Approximate monthly cost	The Local Impact project Data Model includes a few questions on the main source of energy being used by a facility (Hospital, Education). Additional information on energy can be collected from Zesco as recommended by the IDP.
Environment and Climate Change	No	N/A	The Local Impact project Data Model is not collecting data specifically on Climate Change. This data will be collected from secondary sources of data in other institutions working on Climate Change related projects.

Remote Mapping

Remote mapping is the process of digitization or tracing features from satellite imagery to generate a feature dataset.  OpenStreetMap contributors have open access to several imagery sources such as Bing Aerial Imagery, Maxar Premium Imagery, Esri World Imagery, Mapbox Satellite, and some other custom imagery sources to edit OSM around the world.

HOT has also developed a number of tools that are used for mapping activities including the HOT Tasking Manager. This tool is one of the most popular in the OSM community for collaborative remote mapping of various locations around the world for several remote-mapped data use cases. Remotely mapped data can be used in GIS software for service delivery planning purposes and disaster response activities, among others.

At the start of the project, HOT reviewed all available and remote-mapped OSM data in the Muchinga and Central provinces. Using JOSM, HOT concluded that a large number of buildings and roads were not mapped using the most relevant satellite imagery for the project - DigitalGlobe (now known as Maxar) using  the imagery. As such, YouthMappers and HOT volunteers subsequently mapped all districts in both Muchinga and Central provinces.  Using

satellite imagery, the YouthMappers, and the HOT volunteers captured both roads and buildings in these provinces.

Using the Tasking Manager, HOT also re-aligned the previous remotely mapped buildings and roads with the 2019 aerial imagery, and the districts in Muchinga and Central Province were divided into tasks each sized 0.02km2 which were used during the validation process where mapping volunteers picked one square at a time and reviewed the existing digitization. Using JOSM’s Select Tool, mappers would drag and reposition building footprints to be aligned with buildings as seen in the  imagery. Newly constructed building structures were also mapped using the building tool in JOSM.

Field Data Collection

The data collection was conducted in both Central and Muchinga Provinces in 19 districts Dis.

Field Data Collection Central Province

Service Delivery Data collection training took place in Chibombo district of Central  Province from the 21st  of June to the 24th of June 2022 as a pilot. During the exercise, two district planners were  present  as supervisors in the first phase which had  14 Data Collectors from seven (7) wards. The seven wards were; Chibombo, Chikobo, Cholokelo, Chunga, Kakoma, Katuba, and Mungule. Additionally, each ward had two (2) volunteers from their respective communities. The  second phase of data collection for the Chibombo district for the remaining 14 wards took place from 7th July 2022 to 19th July 2022 most of the wards were present except Malambanyama and Lunjofya. The data collection in Central Province took place in the following districts, Chibombo, Chisamba, Chitambo, Kabwe, Kapiri Mposhi, Luano, Mumbwa, Mkushi, Ngabwe, Serenje and Shibuyunji

The objectives of the training were;

• To train the participants on how to collect data
• To have data collectors understand the tools of data collection
• To have participants learn how to use ODK and OSMAnd
• To help participants understand the two forms ( Water for and waste collection form)

At the end of the 3 days of training the data collector understood how to collect the data and how to use the data collection application (ODK and OSMAnd).

What went well

1. All participants attended the training from the first day to the last day.
2. Participants were able to learn, understand and use the ODK and OSMAnd applications.
3. Participants were able to understand the data collection forms and were able to practice them in local languages.
4. All participants were able to collect data and meet their daily targets.

The table below provides water points and waste points that were collected per district in Central Province.

Name of the district	Number data collectors	Number of wards	Water Points	Waste Points
Chibombo	42	21	3671	135
Chisamba	24	12	1278	33
Serenje	42	21	1397	76
Kabwe	58	29	1815	1059
Shibuyunji	24	12	1449	12
Chitambo	20	10	1589	38
Kapiri Mposhi	36	18	2798	199
Mumbwa	42	21	1195	80
Ngabwe	16	8	508	13
Mkushi	36	18	903	131

Field Data Collection Muchinga Province 

Data collection in Muchinga province took place from 25th August 2022 to 4th October 2022. Data collection took place in all 8 districts in Muchinga province namely; Kachibiya, Nkonde, Mpika, Chinsali, Isoka, Mafinga, Lavushimanda, and Shiwan’gandu. During the data collection in Muchinga province, 208 data collectors who were youths participated in the exercises. The data collectors came from the wards within the districts and each ward was represented by two youths a male and a female. The data collators were trained for 3 days before going into the field. During the training, the data collectors were taught how to use the tools for data collection such as the Open Data Kit (ODK), OSMand understanding the forms for both water and waste, and data collection ethics. The data collection lasted for 10 days and 3 days of training. The objectives of the training were;

• To train the participants on how to collect data
• To have data collectors understand the tools of data collection
• To have participants learn how to use ODK and OSMAnd
• To Understand their assigned working area (Ward)
• To help participants understand the two forms ( Water for and waste collection form)

What went well

1. All participants attended the training from the first day to the last day.
2. Participants were able to learn, understand and use the ODK and OSMAnd applications.
3. Participants were able to understand the data collection forms and were able to practice them in local languages.
4. All participants were able to collect data and meet their daily targets.

The data collection was supervised by the officers from the councils such as the   social economic planners, district planners, and the surveyors from the district.  The data was collected using tablets and a central server was set up where the data collectors were uploading the forms every time they were done collecting the information. The table below shows the data that was collected in each of the districts in Muchinga province.

Name of the district	Number data collectors	Number of wards	Waste point	Water points
Nakonde	30	15	142	703
Kachibiya	20	10	7	1156
Isoka	28	14	37	1216
Mpika	24	12	24	511
Shiwang’andu	20	10	22	176
Lavushamanda	12	6	39	970
Mafinga	26	13	5	1181
Chinsali	30	12	41	796

Data Cleaning

The data cleaning team was made up of 8 HOT volunteers and was led by the mapping supervisors. Data cleaners were given training on data cleaning processes; the training focused on advanced features and functionalities of JOSM software that would assist in the cleaning of the data filter and modifying and merging the data collected with the existing OSM data. Some of these features included expert modes, filters, relations, and plugins, such as a To-do list and Open Data. Each HOT  volunteer  was assigned a portion or part of the data that needed to be cleaned based on the district's sections that were initially created to carry out the field data collection. Our team used Google Drive to collaborate on data cleaning, and data sharing and enable final authorization of the completed work before uploading the data to OpenStreetMap. This workflow made it possible for our team to work both in-person and remotely on cleaning and preparing the datasets for analysis and visualization. Our team also used Google Sheets to track the data cleaning and upload progress to OSM. Whatsapp was also used to communicate and raise questions or issues regarding data cleaning via screenshots with Team Leads to enable them to identify solutions faster.

Additional tools that were used during the data cleaning process include:    

• Java OpenStreetMap (JOSM)

JOSM (Java OpenStreetMap Editor) is a desktop application for editing OpenStreetMap; it is a free software created in Java. The notable feature of JOSM is importing GPX files (GPS tracks), working with aerial imagery (including WMS, TMS, and WMTS protocols), support for multiple cartographic projections, layers, relations editing, data validation tools, data filtering, offline work, presets and rendering styles.

• Quantum Geographic Information System (QGIS)

QGIS (Quantum Geographic Information System) is a free, open-source software that allows users to create, edit, visualize, analyze, and publish geospatial information.

There are many benefits to using QGIS. First, the software offers many free online resources and maps available to download. QGIS also accepts many vector file formats. Finally, there are a variety of plug-ins for potential use, and there are always new plug-ins being created. Plug-ins are extra applications that can be downloaded to complete a specific task that is not easily accomplished otherwise.

• OpenStreetMap Changeset Analyzer (OSMCHA)

Quality Assurance and Quality Control is the process by which mappers, and OpenStreetMap contributors in general, check data to ensure that all information uploaded to OSM meets minimum required standards for use. OpenStreetMap being a free and open platform that anyone can use and edit, it is critical to the sustainability of open data and OSM that everyone participates in the quality control & assurance process – from remote mapping, and field data collection, to data cleaning to long-term maintenance of existing OSM data. One of the tools that HOT use is  OSMCha( OpenStreetMap Changeset Analyzer), which is a tool designed to review uploads and changes to OSM data, primarily to prevent vandalism and bad edits made to map data. This tool allows users to filter by username, location, dates of upload, and other metadata features. OSMCha is useful for monitoring the contribution of mappers continuously. Notable observed data quality issues during the data cleaning process include;

• Incompleteness of the attribute data (Same of the names for POI were missing from the data set.
• Bad geometry (some mappers were not mapping buildings according to the shape of the building
• Bad values (some data collectors were inputting wrong values in the questionnaire)  

Overview of Data Cleaning process

1. A raw data file is assigned to a Mapping Supervisor for cleaning.
2. A final cleaned version is sent over to the Senior Mapping Supervisor to be validated for uploading into OSM.
3. Senior Mapping Supervisor reviews the final data and authorises upload to OpenStreetMap.

Findings:

• Missing features:

During the remote mapping, some of the features like roads were missed and this can result in unconnected ways which is a serious data quality issue that affects routing using OSM data. When mapping in OSM particularly ways in this case roads should not be left hanging, roads must be connected to each other..

• Temporal inconsistency of the map features: Some of the buildings that were remotely mapped did not exist on the ground and some mapped buildings were not aligned with the shape of the buildings.

Some of the mapped buildings no longer  exist, especially in rural areas. Some of the buildings were demolished and new buildings were constructed. This issue is mainly a result of changes in the temporal characteristics of the satellite imagery used during mapping.

Solutions:

• Validation: Missing features were always added during the validation process.
• Temporal inconsistencies were resolved by updating the data to the most recent Maxar imagery. Feedback: HOT continuously emphasized the data collectors and mappers to map what is on the image and apply local knowledge only when available.

Lessons Learned

Implementation of the “Service Delivery for Central and Muchinga Province  Mapping” project has been an immense learning opportunity for HOT. From the inception work,  creating a workflow, documenting the methodology and OSM data to designing and  stakeholder meetings with a number of local communities, provincial planners, district planners, and ward development committees, HOT developed new approaches to better identifying the best way of collecting data by making sure that stakeholders understand what the project is all about. With respect to engaging different government ministries and government agencies, our team gained a lot of understanding about what works well when engaging with different stakeholders on data sharing.  The focus of this section is to highlight key lessons learned during the implementation of this project and recommendations to improve the design and implementation of similar projects in the future:

To make a project more useful and valuable, review project outcomes and deliverables from previous projects that are similar in scope to better understand the end user’s immediate needs.

HOT gained a lot of effective and viable information from The DAI-led Local Empowerment for Government Inclusion and Transparency (LEGIT) program in Liberia. This helped to better understand and correlate with the stakeholder’s needs. For this reason, Local Impact  organized  a ‘Provincial data digitization meeting Muchinga and Central Province ’ during which project stakeholders were able to present and discuss specific challenges with Zone Mapping challenges  that may cause the project to experience a number of setbacks and identify solutions for how these set-backs can be addressed and avoided.

End-user involvement in the planning and development of the data model is critical.

Because the creation of a data model is vital to the design and implementation of a mapping project, it is important to engage end users in its development as early as possible. Data models outline the features and attributes needing to be collected during field activities and the logic behind them; it is critical then that these models are detailed and indicative of the possible map visualizations and analyses that can result from them. For this reason, the sooner end users are involved in the process of developing a data model, the more time there is for them to provide input, guidance, and the need for specific data to be collected by data collectors on the ground during field mapping activities.

In light of the above, Local Impact HOT organized a series of data model development meetings  and developed documents that would solicit their feedback on the data intended to be collected on the ground. During the meeting, HOT highlighted the importance of collecting spatial data on water and waste, because of the gap  that was missing in the  desktop data collection with the stakeholders.

Holding multiple community stakeholder meetings is essential to entering and working with the local community and being able to implement field mapping activities without continuous resistance.

Community stakeholder meetings and field mapping activities are essential components of successful community development projects. These activities help to ensure that the needs and perspectives of local residents are taken into account, and that project outcomes are aligned with the goals and values of the community.

Engaging local stakeholders is vital for effective data collection and monitoring. Local stakeholders are also essential when mobilizing the most relevant participants for the activity. Involving local residents, NGOs, CSO, and government officials  in the planning and implementation of community development projects, it ensures that the project is responsive to their needs and concerns. This helps to build trust and support for the project and can increase the likelihood of success.

Identifying local resources during mapping helps the local people  to identify important natural resources, cultural assets, and other community resources that can be leveraged to support the project. This information can be used to develop strategies that build on the strengths of the community, rather than relying solely on external resources.

Building capacity is vital for the sustainability of the community and the ownership of the project implementation. Community stakeholder meetings and field mapping activities can help to build the capacity of local partners  to participate in community development initiatives. This can include developing skills in project management, data collection and analysis, and community organizing.

Enhancing project outcomes need to take into consideration different  stakeholders in the planning and implementation of community development projects. HOT learned that you can increase the likelihood of achieving positive outcomes that are aligned with the goals and values of the community, only if all the partners are involved. This can help to ensure that the project has a lasting impact and benefits the community.

Overall, community stakeholder engagement and field mapping activities are important tools for building strong, resilient, and sustainable communities. They help to ensure that community development projects are inclusive, responsive, and effective, and can lead to better outcomes for all stakeholders involved.

The table below shows challenges that were encountered  during the implementation of the project and how they were resolved.

CHALLENGES	HOW THEY WERE RESOLVED
Lack of Computers and internet connectivity in some councils	Local Impact managed to provide computers for the council officers during the training.
Some of the printed ward maps that were used were not clear enough to enable the community in identifying some of the features	Encouraged more people to take place in identifying the features in the ward.
Communication on the training of the data collectors was not done in good time.	The training was adjusted to accommodate the data collectors that came a day after the start of the training.
The monitoring of data collectors during the fieldwork was difficult due to limited time.	With the help of the planners, the HOT team managed to visit most of the data collectors.
Most of the GIS Technicians that were taught Zone and Resource mapping were unfamiliar with QGIS.	Training in the use of QGIS was offered to the planners during the workshops in Central and Muchnga

Conclusions

The Project provided much-needed data for the development of the integrated development plan (IDP), most of the districts did not have enough information for the development of the IDP, particularly on geospatial data.  The project helped them to move fast and it was less costly to the district to have this information because Local Impact through HOT helped in the data collection and data sharing process. Planners also acquired knowledge of OpenStreetMap and the use of QGIS. The provision of this data will  not only help in the preparation of the IDP but the data is helping in other planning programs within different districts.

Recommendations

• More training in QGIS  with the planners is needed to make sure that there is suitability for the use of the data that was provided to them.
• There is a need to have a plan for data sharing from the national level to the districts.
• Training for data use
• Timeline - how to phrase the need for adequate time for data collection and QA

Annexes

Annex 1 Maps Development for IDP Process

Map showing the Service Delivery Locations in Chibombo District.

Map showing the Distribution of Water Points in Chibombo District.

Map showing the Distribution of Waste Points in Chibombo District.